[
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    "text": "---\ntitle: \"ECDP '24 Poster\"\nid: \"poster\"\ncategory: \"conference\"\ntags: [\"poster\", \"ner\", \"pathology\"]\nrelated: [\"devfest\", \"python\"]\ndate: \"2024-06-05\"\ndescription: \"Poster in 20th European Congress on Digital Pathology 2024\"\n---\n\nI'm excited to share that [our poster](https://lnkd.in/dJ2WKKRF) is in 20th European Congress on Digital Pathology 2024 | \nECDP2024! ✨\n\nFirst and foremost, I owe my heartfelt thanks to hashtag#MemorialPathology Memorial Healthcare Group and Dr. Serdar \nBalcı for involving me in this project and for all their invaluable support and assistance throughout this journey.",
    "source": "2024-06-05-ecdp_24_poster.md",
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  {
    "text": "Our study highlights the importance of extracting information from pathology reports using Named Entity Recognition \n(NER). We conducted extensive tests utilizing various models (BERT Models and GPT) to ensure robust results.\n\nYou can try our GPTs: \"PathText\" [here](https://lnkd.in/dzyC6PJm). ✨ And you may find a [video](https://www.linkedin.com/posts/activity-7204810402281512960-_w5D?utm_source=share&utm_medium=member_desktop) \nabout that. It is currently in the beta testing phase, and we aim to develop it into a more comprehensive product.",
    "source": "2024-06-05-ecdp_24_poster.md",
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  {
    "text": "---\ntitle: \"Optimization Algorithms in Neural Networks\"\nid: \"optimization\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"backpropagation\", \"cs231n\"]\nrelated: [\"dl\"]\ndate: \"2020-10-14\"\ndescription: \"Optimization Algorithms in Neural Network will be explained\"\n---\n\nIn this post, Optimization Algorithms in Neural Network will be explained. Thanks to Stanford University, the lecture \nvideos of cs231n can be found in YouTube and all materials are freely available (see the references). \n\n# First Order Optimization Algorithms\n\n- SGD\n- SGD with Momentum\n- SGD with Nesterov Momentum\n- AdaGrad\n- RMSProp\n- Adam",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_0",
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    "text": "## SGD\n\n    w = w - learningrate * gradient\n    \n- Vanilla update. \n- The simplest form of update is to change the parameters along the negative gradient direction \n- Since the gradient indicates the direction of increase, but we usually wish to minimize a loss function\n    \n## SGD with Momentum\n\n    velocity = 0\n    velocity = momentum * velocity - learningrate * gradient\n    w = w + velocity\n\n- With Momentum update, the parameter vector will build up velocity in any direction that has consistent gradient.\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/sgd_vs_momentum.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 1: SGD vs SGD with Momentum</div>\n</div>",
    "source": "2020-10-14-optimization_algorithms.md",
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    "text": "## SGD with Nesterov Momentum\n\n    old_velocity = velocity\n    velocity = momentum * velocity - learningrate * gradient\n    w = w + (-momentum) * old_velocity + (1- (-momentum)) * velocity\n\n- https://arxiv.org/pdf/1212.0901v2.pdf : ADVANCES IN OPTIMIZING RECURRENT NETWORKS\n- http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf : RNN",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "## AdaGrad\n\nRef: https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf (-)\nRef: https://arxiv.org/pdf/1212.5701.pdf *\n\n    gradient_squared = 0 \n    gradient_squared += gradient**2\n    w += - learning_rate * gradient / (np.sqrt(gradient_squared) + eps)\n\n**Pros:** *\n\nWhile there is the hand tuned global learning rate, each dimension has its own dynamic rate. \n\nSince this dynamic rate grows with the inverse of the gradient magnitudes:\n    - large gradients have smaller learning rates\n    - small gradients have large learning rates. \n\nThis is very beneficial for training deep neural networks since the scale of the gradients in each layer is often \ndifferent by several orders of magnitude, so the optimal learning rate should take that into account.",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
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    "text": "Accumulation of gradient in the denominator has the same effects as annealing, reducing the learning rate over time. \n\n**Cons:** *",
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    "text": "**Cons:** * \n\n- This method can be sensitive to initial conditions of the parameters and the corresponding gradients. \n- If the initial gradients are large, the learning rates will be low for the remainder of training. \n- This can be combatted by increasing the global learning rate, making the ADAGRAD method sensitive to the choice of \nlearning rate. \n- Due to the continual accumulation of squared gradients in the denominator, the learning rate will continue to decrease \nthroughout training, eventually decreasing to zero and stopping training completely.\n- Another exp:  A downside of Adagrad is that in case of Deep Learning, the monotonic learning rate usually proves too \naggressive and stops learning too early. **",
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  {
    "text": "## RMS Prop\n\n    gradient_squared = 0 \n    gradient_squared = decay_rate * gradient_squared + (1 - decay_rate) * gradient**2\n    w += - learning_rate * gradient / (np.sqrt(gradient_squared) + eps)\n\n- The RMSProp update adjusts the Adagrad method in a very simple way in an attempt to reduce its aggressive, monotonically \ndecreasing learning rate. \n- In particular, it uses a moving average of squared gradients.\n- decay_rate is a hyperparameter and typical values are [0.9, 0.99, 0.999].",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_6",
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    "text": "- Notice that the w+= update is identical to Adagrad.\n- But the gradient_squared variable is a “leaky”. (has decay rate)\n- Hence, RMSProp still modulates the learning rate of each weight based on the magnitudes of its gradients. \nIt has a beneficial equalizing effect.\n - But unlike Adagrad, the updates do not get monotonically smaller.\n\n\n# Adam\n\nRef: https://arxiv.org/pdf/1412.6980.pdf",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "# Adam\n\nRef: https://arxiv.org/pdf/1412.6980.pdf\n\n    # t is your iteration counter going from 1 to infinity\n    first_moment, second_moment = 0, 0\n    for t in range(iterations):\n        first_moment = beta1 * first_moment + (1-beta1) * gradient\n        first_unbias = first_moment / (1-beta1**t)\n        \n        second_moment = beta2 * second_moment + (1-beta2)*(gradient**2)\n        second_unbias = second_moment / (1-beta2**t)\n        \n        w += - learning_rate * first_unbias / (np.sqrt(second_unbias) + eps)\n        \nThe method computes individual adaptive learning rates for different parameters from estimates of first and second \nmoments of the gradients; the name Adam is derived from adaptive moment estimation.\n\nWithout bias:",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "Without bias: \n\nAt very first time step, second moment is zero. After one update teh second moment will still close to zero. When we\ndivide by second moment, we make a very large step at the beginning.  But this big step is not related with the geometry of the \nloss function, it is the nature of the formula. \n\nBias correction term help to aviod the very large step at the beginning. \n\nYou can start with these parameters:\n\n    beta1 = 0.9\n    beta2 = 0.999\n    lr = 1e-3 or 1e-5 \n    \n    \nRMSProp with momentum generates its parameter updates using a momentum on the rescaled gradient, whereas Adam updates are\ndirectly estimated using a running average of first and second moment of the gradient.",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_9",
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    "text": "RMSProp also lacks a bias-correction term; this matters most in case of a value of beta2 close to 1 (required in\ncase of sparse gradients), since in that case not correcting the bias leads to very large stepsizes and\noften divergence. \n\nDifferent Models comparison: {'sgd': 5e-3, 'sgd_momentum':  5e-3, 'rmsprop': 1e-4, 'adam': 1e-3}\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/different_models.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 2: Different models</div>\n</div>\n\nDifferent Models comparison: {'sgd': 1e-3, 'sgd_momentum':  1e-3, 'rmsprop': 1e-3, 'adam': 1e-3}",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "Different Models comparison: {'sgd': 1e-3, 'sgd_momentum':  1e-3, 'rmsprop': 1e-3, 'adam': 1e-3}\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/different_models_same_lr.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 3: Different models with the same learning rate</div>\n</div>",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "## Change LR\n\nyou can change learning rate during the training:\n\n- Step Decay\n- Exponential Decay\n- 1/t decay\n\nStart with no decay. Select the best learning rate. Check the loss plot and decide where you need decay. \n\n#### 'sgd' : \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/sgd_lr.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 4: SGD, learning rates: [1e-4, 5e-3, 3e-3, 1e-3, 1e-2]</div>\n</div>\n\n\n#### 'sgd_momentum':\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/sgd_moment_lr.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 5: SGD with Momentum, learning rates: [1e-4, 5e-3, 3e-3, 1e-3, 1e-2]</div>\n</div>\n\n#### 'rmsprop':",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_12",
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  {
    "text": "#### 'rmsprop': \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/rms_lr.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 6: RMSProp, learning rates: [1e-4, 5e-4, 5e-3, 1e-3, 1e-2]</div>\n</div>\n\n\nRMS Prop needs smaller lr: [1e-5, 5e-5, 5e-4, 1e-4]\n- running with  5e-05: (Epoch 5 / 5) train acc: 0.591000; val_acc: 0.359000\n- running with  0.0001: (Epoch 5 / 5) train acc: 0.557000; val_acc: 0.376000\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/rms_lr_smaller.png\" width=\"75%\">\n  <div class=\"figcaption\">RMSProp, smaller learning rates</div>\n</div>\n\n####'adam':",
    "source": "2020-10-14-optimization_algorithms.md",
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  {
    "text": "####'adam':\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/optimization_images/adam_lr.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 7: Adam, learning rates: [1e-4, 5e-4, 5e-3, 1e-3, 1e-2]</div>\n</div>",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_14",
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  {
    "text": "## Model Ensembles\n\nRef: https://www.youtube.com/watch?v=EK61htlw8hY&ab_channel=TTIC\n\nOne disadvantage of model ensembles is that they take longer to evaluate on test example. \nAn interested reader may find the recent work from Geoff Hinton on “Dark Knowledge” inspiring, where the idea is to \n“distill” a good ensemble back to a single model by incorporating the ensemble log likelihoods into a modified objective.\n\nRef: https://www.youtube.com/watch?v=_JB0AO7QxSA&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=7&ab_channel=StanfordUniversitySchoolofEngineering\n\n1. Train multiple independent models \n2. At test time average their results \n\n# Second Order Optimization Algorithms",
    "source": "2020-10-14-optimization_algorithms.md",
    "type": "markdown",
    "chunk_id": "2020-10-14-optimization_algorithms.md_15",
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  {
    "text": "1. Train multiple independent models \n2. At test time average their results \n\n# Second Order Optimization Algorithms\n\n- https://arxiv.org/pdf/1311.2115.pdf (Quasi Newton)\n- https://research.google/pubs/pub40565/ (Large Scale Distributed Deep Networks)\n- https://en.wikipedia.org/wiki/Limited-memory_BFGS (LBFGS)\n\n# References\n\n- https://www.youtube.com/watch?v=_JB0AO7QxSA&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=7&ab_channel=StanfordUniversitySchoolofEngineering\n- https://cs231n.github.io/\n\n# Key Words\nSGD, Momentum, Learning Rates, RMSProp, ADam, Optimization, Neural Networks",
    "source": "2020-10-14-optimization_algorithms.md",
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    "text": "---\ntitle: \"Python Programming Basics\"\nid: \"python\"\ncategory: \"machine-learning\"\ntags: [\"python\", \"programming\", \"basics\", \"tutorial\", \"ai-generated\"]\nrelated: [\"ml\"]  # Added your trial note\ndate: \"2025-11-08\"\ndescription: \"Essential Python programming concepts for beginners\"\n---\n\n# Python Programming Basics\n\n**This article is generated by Claude Opus 4.5**\n\n## Overview\n\nPython is a high-level, interpreted programming language known for its simplicity and readability. It's one of the most popular languages for data science, machine learning, web development, and automation.",
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    "text": "## Why Python?\n\n- **Easy to Learn**: Clean, readable syntax\n- **Versatile**: Web dev, data science, automation, AI\n- **Large Community**: Extensive libraries and support\n- **Cross-platform**: Works on Windows, Mac, Linux\n- **Free and Open Source**: No licensing costs\n\n## Getting Started\n\n### Installation\n\nVisit [python.org](https://www.python.org/downloads/) to download Python.\n\n```bash\n# Check if Python is installed\npython --version\n\n# Or\npython3 --version\n```\n\n### Your First Program\n\n```python\n# hello.py\nprint(\"Hello, World!\")\n```\n\nRun it:\n```bash\npython hello.py\n```",
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  {
    "text": "## Basic Syntax\n\n### Variables\n\n```python\n# Variables (no declaration needed)\nname = \"Alice\"\nage = 25\nheight = 5.6\nis_student = True\n\n# Multiple assignment\nx, y, z = 1, 2, 3\n```\n\n### Data Types\n\n```python\n# Integers\ncount = 10\n\n# Floats\nprice = 19.99\n\n# Strings\nmessage = \"Hello, Python!\"\n\n# Booleans\nis_active = True\n\n# Lists\nfruits = [\"apple\", \"banana\", \"cherry\"]\n\n# Tuples (immutable)\ncoordinates = (10, 20)\n\n# Dictionaries\nperson = {\n    \"name\": \"Alice\",\n    \"age\": 25,\n    \"city\": \"New York\"\n}\n\n# Sets (unique elements)\nunique_numbers = {1, 2, 3, 4}\n```",
    "source": "python.md",
    "type": "markdown",
    "chunk_id": "python.md_2",
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    "text": "## Control Flow\n\n### Conditionals\n\n```python\n# if-elif-else\nage = 18\n\nif age < 18:\n    print(\"Minor\")\nelif age == 18:\n    print(\"Just became an adult\")\nelse:\n    print(\"Adult\")\n```\n\n### Loops\n\n```python\n# for loop\nfruits = [\"apple\", \"banana\", \"cherry\"]\nfor fruit in fruits:\n    print(fruit)\n\n# range()\nfor i in range(5):  # 0 to 4\n    print(i)\n\n# while loop\ncount = 0\nwhile count < 5:\n    print(count)\n    count += 1\n```",
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    "text": "## Functions\n\n```python\n# Basic function\ndef greet(name):\n    return f\"Hello, {name}!\"\n\nmessage = greet(\"Alice\")\nprint(message)  # Hello, Alice!\n\n# Default parameters\ndef power(base, exponent=2):\n    return base ** exponent\n\nprint(power(3))      # 9 (3^2)\nprint(power(3, 3))   # 27 (3^3)\n\n# Multiple return values\ndef get_stats(numbers):\n    return min(numbers), max(numbers), sum(numbers)\n\nminimum, maximum, total = get_stats([1, 2, 3, 4, 5])\n```",
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  {
    "text": "## Lists and List Comprehensions\n\n```python\n# List operations\nnumbers = [1, 2, 3, 4, 5]\n\nnumbers.append(6)        # Add to end\nnumbers.insert(0, 0)     # Insert at position\nnumbers.remove(3)        # Remove value\nnumbers.pop()            # Remove last item\n\n# Slicing\nfirst_three = numbers[:3]\nlast_two = numbers[-2:]\n\n# List comprehension\nsquares = [x**2 for x in range(10)]\n# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n\nevens = [x for x in range(20) if x % 2 == 0]\n# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n```",
    "source": "python.md",
    "type": "markdown",
    "chunk_id": "python.md_5",
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    "text": "## Dictionaries\n\n```python\n# Creating and accessing\nstudent = {\n    \"name\": \"Alice\",\n    \"age\": 20,\n    \"grades\": [85, 90, 92]\n}\n\nprint(student[\"name\"])           # Alice\nprint(student.get(\"age\"))        # 20\nprint(student.get(\"email\", \"N/A\"))  # N/A (default)\n\n# Adding/updating\nstudent[\"email\"] = \"alice@example.com\"\nstudent[\"age\"] = 21\n\n# Iterating\nfor key, value in student.items():\n    print(f\"{key}: {value}\")\n```",
    "source": "python.md",
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  {
    "text": "## Classes and Objects\n\n```python\n# Define a class\nclass Dog:\n    def __init__(self, name, age):\n        self.name = name\n        self.age = age\n    \n    def bark(self):\n        return f\"{self.name} says Woof!\"\n    \n    def get_age_in_dog_years(self):\n        return self.age * 7\n\n# Create objects\nmy_dog = Dog(\"Buddy\", 3)\nprint(my_dog.bark())                    # Buddy says Woof!\nprint(my_dog.get_age_in_dog_years())   # 21\n```",
    "source": "python.md",
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  {
    "text": "## File Handling\n\n```python\n# Writing to a file\nwith open(\"output.txt\", \"w\") as file:\n    file.write(\"Hello, World!\\n\")\n    file.write(\"Python is awesome!\")\n\n# Reading from a file\nwith open(\"output.txt\", \"r\") as file:\n    content = file.read()\n    print(content)\n\n# Reading line by line\nwith open(\"output.txt\", \"r\") as file:\n    for line in file:\n        print(line.strip())\n```\n\n## Error Handling\n\n```python\n# try-except\ntry:\n    result = 10 / 0\nexcept ZeroDivisionError:\n    print(\"Cannot divide by zero!\")\nexcept Exception as e:\n    print(f\"An error occurred: {e}\")\nfinally:\n    print(\"This always executes\")\n\n# Raising exceptions\ndef divide(a, b):\n    if b == 0:\n        raise ValueError(\"Divisor cannot be zero\")\n    return a / b\n```",
    "source": "python.md",
    "type": "markdown",
    "chunk_id": "python.md_8",
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    "text": "## Modules and Imports\n\n```python\n# Importing standard library\nimport math\nprint(math.pi)           # 3.141592653589793\nprint(math.sqrt(16))     # 4.0\n\n# Import specific functions\nfrom math import pi, sqrt\nprint(pi)\n\n# Import with alias\nimport numpy as np\nimport pandas as pd\n\n# Your own modules\n# In mymodule.py:\ndef my_function():\n    return \"Hello from module\"\n\n# In main.py:\nimport mymodule\nprint(mymodule.my_function())\n```",
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    "text": "## Useful Built-in Functions\n\n```python\n# len() - length\nprint(len([1, 2, 3]))        # 3\nprint(len(\"Hello\"))          # 5\n\n# type() - check type\nprint(type(42))              # <class 'int'>\n\n# range() - sequence of numbers\nlist(range(5))               # [0, 1, 2, 3, 4]\n\n# enumerate() - index and value\nfor i, fruit in enumerate([\"apple\", \"banana\"]):\n    print(f\"{i}: {fruit}\")\n\n# zip() - combine iterables\nnames = [\"Alice\", \"Bob\"]\nages = [25, 30]\nfor name, age in zip(names, ages):\n    print(f\"{name} is {age}\")\n\n# map() - apply function\nnumbers = [1, 2, 3, 4]\nsquared = list(map(lambda x: x**2, numbers))\n# [1, 4, 9, 16]\n\n# filter() - filter elements\nevens = list(filter(lambda x: x % 2 == 0, numbers))\n# [2, 4]\n```",
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  {
    "text": "## Common Mistakes to Avoid\n\n1. **Indentation Errors**: Python uses indentation (not brackets)\n2. **Mutable Default Arguments**: Don't use mutable defaults in functions\n3. **Not Closing Files**: Use `with` statement\n4. **Index Out of Range**: Check list length before accessing\n5. **Integer Division**: Use `//` for floor division, `/` for float\n\n## Practice Projects\n\n### Beginner\n- Calculator\n- To-do list\n- Number guessing game\n- Password generator\n\n### Intermediate\n- Web scraper\n- Data analyzer\n- Simple game (snake, tic-tac-toe)\n- API consumer",
    "source": "python.md",
    "type": "markdown",
    "chunk_id": "python.md_11",
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    "text": "## Essential Libraries\n\n```python\n# Data Science\nimport numpy as np         # Numerical computing\nimport pandas as pd        # Data manipulation\nimport matplotlib.pyplot as plt  # Visualization\n\n# Web Development\nfrom flask import Flask    # Web framework\nimport requests           # HTTP requests\n\n# Automation\nimport os                 # Operating system\nimport sys                # System-specific\nfrom pathlib import Path  # File paths\n```",
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  {
    "text": "## Resources for Learning\n\n### Online Platforms\n- [Python.org Tutorial](https://docs.python.org/3/tutorial/)\n- [Real Python](https://realpython.com/)\n- [Python for Everybody](https://www.py4e.com/)\n\n### Practice\n- [LeetCode](https://leetcode.com/) - Coding challenges\n- [HackerRank](https://www.hackerrank.com/) - Python track\n- [Project Euler](https://projecteuler.net/) - Math problems\n\n### Books\n- \"Python Crash Course\" by Eric Matthes\n- \"Automate the Boring Stuff with Python\" by Al Sweigart\n- \"Fluent Python\" by Luciano Ramalho (Advanced)",
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  {
    "text": "## Google Colab Example\n\nTry this code in [Google Colab](https://colab.research.google.com):\n\n```python\n# Data analysis example\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Create sample data\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n    'Age': [25, 30, 35, 28],\n    'Score': [85, 90, 78, 92]\n}\n\ndf = pd.DataFrame(data)\nprint(df)\n\n# Plot\nplt.bar(df['Name'], df['Score'])\nplt.title('Student Scores')\nplt.xlabel('Name')\nplt.ylabel('Score')\nplt.show()\n```\n\n## Key Takeaways\n\n- Python is beginner-friendly with clean syntax\n- Strong typing but dynamically typed\n- Indentation matters (use 4 spaces)\n- Rich standard library and ecosystem\n- Great for automation, data science, and web development\n- Practice is essential - code daily!",
    "source": "python.md",
    "type": "markdown",
    "chunk_id": "python.md_14",
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  {
    "text": "## Related Topics\n\n- [Machine Learning](ml.html) - Use Python for ML\n- [NumPy](numpy.html) - Numerical computing library\n- [Data Structures](data-structures.html) - Core CS concepts\n\n## Next Steps\n\n1. Install Python on your machine\n2. Complete a beginner tutorial\n3. Build small projects\n4. Learn a library (NumPy, Pandas, or Flask)\n5. Contribute to open source\n\n---\n\n**Last Updated:** November 8, 2025  \n**Difficulty Level:** Beginner",
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  {
    "text": "---\ntitle: \"LLM FAQ\"\nid: \"faq\"\ncategory: \"genai\"\ntags: [\"evaluation\", \"llm\", \"prompt\", \"finetuning\", \"medium\"]\nrelated: [\"llm\"]\ndate: \"2023-09-18\"\ndescription: \"Q&A for LLM\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/llm-q-a-4c98031d9ea3) \n\nIf you’ve been curious about LLMs and still have questions, this article \nis just for you. Get ready to dive deep into the world of language models!",
    "source": "2023-09-18-LLM_Q_A.md",
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    "text": "---\ntitle: \"Machine Learning Fundamentals\"\nid: \"ml\"\ncategory: \"machine-learning\"\ntags: [\"machine learning\", \"AI\", \"algorithms\", \"supervised learning\", \"ai-generated\"]\nrelated: [\"python\", \"dl\"]  # Added your trial note\ndate: \"2025-11-08\"\ndescription: \"Introduction to machine learning concepts, algorithms, and applications\"\n---\n\n# Machine Learning Fundamentals\n\n**This article is generated by Claude Opus 4.5**\n\n## Overview\n\nMachine Learning (ML) is a subset of artificial intelligence that focuses on building systems that can learn from and make decisions based on data. Instead of being explicitly programmed, these systems improve their performance through experience.",
    "source": "ml.md",
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  {
    "text": "## What is Machine Learning?\n\nMachine Learning enables computers to learn patterns from data without being explicitly programmed for every scenario. It's the foundation of many modern AI applications, from recommendation systems to autonomous vehicles.\n\n### Key Concepts\n\n- **Training Data**: Historical data used to teach the model\n- **Features**: Input variables used to make predictions\n- **Labels**: The output we want to predict (in supervised learning)\n- **Model**: The mathematical representation of patterns in data",
    "source": "ml.md",
    "type": "markdown",
    "chunk_id": "ml.md_1",
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    "text": "## Types of Machine Learning\n\n### 1. Supervised Learning\n\nThe algorithm learns from labeled training data, making predictions based on that data.\n\n**Examples:**\n- Classification (spam detection, image recognition)\n- Regression (price prediction, weather forecasting)\n\n```python\n# Simple supervised learning example with sklearn\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\n\n# Training data\nX = np.array([[1], [2], [3], [4], [5]])\ny = np.array([2, 4, 6, 8, 10])\n\n# Create and train model\nmodel = LinearRegression()\nmodel.fit(X, y)\n\n# Make prediction\nprediction = model.predict([[6]])\nprint(f\"Prediction for 6: {prediction[0]}\")  # Output: ~12\n```",
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    "text": "### 2. Unsupervised Learning\n\nThe algorithm finds patterns in unlabeled data.\n\n**Examples:**\n- Clustering (customer segmentation)\n- Dimensionality reduction (data compression)\n- Anomaly detection\n\n### 3. Reinforcement Learning\n\nThe algorithm learns through trial and error, receiving rewards or penalties.\n\n**Examples:**\n- Game playing (AlphaGo, Chess engines)\n- Robotics\n- Resource optimization",
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  {
    "text": "## Common Algorithms\n\n### Linear Regression\nPredicts continuous values based on linear relationships.\n\n### Logistic Regression\nClassification algorithm for binary outcomes.\n\n### Decision Trees\nTree-like model for classification and regression.\n\n### Neural Networks\nInspired by biological neurons, forms the basis of deep learning.\n\n### Support Vector Machines (SVM)\nFinds optimal boundaries between classes.\n\n### K-Means Clustering\nGroups similar data points together.",
    "source": "ml.md",
    "type": "markdown",
    "chunk_id": "ml.md_4",
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  {
    "text": "## The ML Workflow\n\n1. **Problem Definition**: What are we trying to predict or understand?\n2. **Data Collection**: Gather relevant data\n3. **Data Preprocessing**: Clean and prepare data\n4. **Feature Engineering**: Create meaningful features\n5. **Model Selection**: Choose appropriate algorithm\n6. **Training**: Teach the model with training data\n7. **Evaluation**: Test model performance\n8. **Deployment**: Use the model in production\n9. **Monitoring**: Track performance over time",
    "source": "ml.md",
    "type": "markdown",
    "chunk_id": "ml.md_5",
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  {
    "text": "## Practical Example: Predicting House Prices\n\n```python\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\n\n# Load data\ndata = pd.DataFrame({\n    'size': [1500, 1600, 1700, 1800, 1900],\n    'bedrooms': [3, 3, 4, 4, 5],\n    'age': [10, 15, 20, 5, 8],\n    'price': [300000, 320000, 340000, 380000, 400000]\n})\n\n# Prepare features and target\nX = data[['size', 'bedrooms', 'age']]\ny = data['price']\n\n# Split data\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n# Train model\nmodel = RandomForestRegressor(n_estimators=100)\nmodel.fit(X_train, y_train)",
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  },
  {
    "text": "# Train model\nmodel = RandomForestRegressor(n_estimators=100)\nmodel.fit(X_train, y_train)\n\n# Evaluate\npredictions = model.predict(X_test)\nmse = mean_squared_error(y_test, predictions)\nprint(f\"Mean Squared Error: {mse}\")\n```",
    "source": "ml.md",
    "type": "markdown",
    "chunk_id": "ml.md_7",
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    "text": "## Resources for Learning\n\n### Online Courses\n- [Coursera: Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning)\n- [Fast.ai: Practical Deep Learning](https://www.fast.ai/)\n\n### Books\n- \"Hands-On Machine Learning\" by Aurélien Géron\n- \"Pattern Recognition and Machine Learning\" by Christopher Bishop\n\n### Practice Platforms\n- [Kaggle](https://www.kaggle.com) - Competitions and datasets\n- [Google Colab](https://colab.research.google.com) - Free GPU for experiments",
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    "text": "## Key Takeaways\n\n- Machine Learning enables computers to learn from data\n- Three main types: Supervised, Unsupervised, and Reinforcement Learning\n- The ML workflow is iterative and requires continuous improvement\n- Start with simple algorithms before moving to complex ones\n- Practice is essential - work on real projects and datasets\n\n## Related Topics\n\n- [Deep Learning](dl.html) - Neural networks and advanced architectures\n- [Python Basics](python.html) - Programming fundamentals for ML\n- [Statistics](statistics.html) - Mathematical foundations",
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  {
    "text": "## Tools and Libraries\n\n- **Scikit-learn**: General-purpose ML library\n- **TensorFlow**: Deep learning framework\n- **PyTorch**: Deep learning framework\n- **Pandas**: Data manipulation\n- **NumPy**: Numerical computing\n\n---\n\n**Last Updated:** November 8, 2025  \n**Difficulty Level:** Beginner to Intermediate",
    "source": "ml.md",
    "type": "markdown",
    "chunk_id": "ml.md_10",
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    "text": "---\ntitle: \"Word Vectors\"\nid: \"wordvector\"\ncategory: \"genai\"\ntags: [\"vectors\"]\nrelated: [\"dl\", \"llm\"]\ndate: \"2023-01-01\"\ndescription: \"Notebook for word vectors\"\n---\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/2023-01-01-Word_Vectors.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>",
    "source": "2023-01-01-word_vectors.md",
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    "text": "---\ntitle: \"Temperature\"\nid: \"temperature\"\ncategory: \"genai\"\ntags: [\"temperature\", \"llm\", \"video\"]\nrelated: [\"configuration\"]\ndate: \"2025-11-08\"\ndescription: \"A brief description of temperature parameter\"\n---\n\n# Temperature \n\n**Temperature** is a parameter which is injected into the **softmax function**, enabling the users to manipulate the \noutput probabilities. It helps us to control the **creativeness** of a Large Language Model.\n\nThe range of the temperature parameter is defined as 0 and 1 in OpenAI documentation. In the context of Cohere, \ntemperature values fall within the range of 0 to 5. _See the references below._\n\n-----------------------------------------------------\n\nThis is the original softmax function:",
    "source": "2023-10-16-Temperature_parameter.md",
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  {
    "text": "-----------------------------------------------------\n\nThis is the original softmax function: \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/temperature_images/softmax.PNG\" width=\"50%\">\n  <div class=\"figcaption\"> </div>\n</div>\n\n\nWhen we add Temperature parameter:\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/temperature_images/softmax_temp.PNG\" width=\"50%\">\n  <div class=\"figcaption\"> </div>\n</div>\n\nRemember that zj is the output of the neural network: it is a floating number. If you want to learn more about \nsoftmax function, read [here](https://github.com/pelinbalci/Intro_Deep_Learning/blob/master/Intro_NN/notes/1_Perceptron_math.md#multiclass-classification--softmax:~:text=Multiclass%20Classification%20%26%20Softmax).",
    "source": "2023-10-16-Temperature_parameter.md",
    "type": "markdown",
    "chunk_id": "2023-10-16-Temperature_parameter.md_1",
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    "text": "-----------------------------------------------------\n\n- As Temperature approaches 0, the output probabilities become more \"sharp\". One of the probability will be close to 1.\n- As Temperature increases, the output probabilities become more \"flat\" or \"uniform\", reducing the difference between the probabilities of different elements.\n\nIf we want repetitive answers, and no creativity at all, we can decrease the Temperature. If we want more creative answers, we can increase it.\n\n-----------------------------------------------------\n\n# Example \n\nLet's imagine that our corpus has only 5 words: [\"donut\", \"cake\", \"apple\", \"juice\", \"book\"]\n\nThe prediction of next token of given sentence: \"At the table, there is a delicious\" will be one of the words in the corpus.",
    "source": "2023-10-16-Temperature_parameter.md",
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  {
    "text": "The prediction of next token of given sentence: \"At the table, there is a delicious\" will be one of the words in the corpus. \n\nThese are the original results: \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/temperature_images/softmax_df.PNG\" width=\"50%\">\n  <div class=\"figcaption\"> </div>\n</div>\n\nYou can try different temperature values to see how the output changes.\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/temperature_slider.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>\n\nListen my YouTube Vide from [here](https://youtu.be/KbUPOJ8Fmzs)\n\nHappy Learning! :)",
    "source": "2023-10-16-Temperature_parameter.md",
    "type": "markdown",
    "chunk_id": "2023-10-16-Temperature_parameter.md_3",
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  {
    "text": "Listen my YouTube Vide from [here](https://youtu.be/KbUPOJ8Fmzs)\n\nHappy Learning! :)\n\n# References\n- [1] https://platform.openai.com/docs/api-reference/audio/createTranscription#audio/createTranscription-temperature\n- [2] https://txt.cohere.com/llm-parameters-best-outputs-language-ai/\n- [3] https://peterchng.com/blog/2023/05/02/token-selection-strategies-top-k-top-p-and-temperature/",
    "source": "2023-10-16-Temperature_parameter.md",
    "type": "markdown",
    "chunk_id": "2023-10-16-Temperature_parameter.md_4",
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    "text": "---\ntitle: \"Introduction to Agentic Architecture\"\nid: \"agentic-architecture\"\ncategory: \"genai\"\ntags: [\"agents\", \"LLM\", \"autonomous systems\", \"AI architecture\"]\nrelated: [\"llm\"]\ndate: \"2025-11-25\"\ndescription: \"An overview of agentic AI systems, frameworks, and protocols that enable autonomous LLM-powered agents\"\n---\n\n# Introduction to Agentic Architecture\n\n**This article is generated by Claude Opus 4.5**\n\n## Overview\n\nAgentic architecture represents a paradigm shift in how we build AI systems. Instead of simple prompt-response interactions, agentic systems can plan, reason, use tools, and execute multi-step tasks autonomously. These systems transform Large Language Models (LLMs) from passive responders into active problem-solvers.",
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    "text": "## What Makes an AI System \"Agentic\"?\n\nAn agentic system exhibits several key characteristics that distinguish it from traditional LLM applications:\n\n- **Autonomy**: The ability to make decisions and take actions without constant human intervention\n- **Tool Use**: Integration with external tools, APIs, and data sources to accomplish tasks\n- **Planning**: Breaking down complex goals into manageable steps\n- **Memory**: Maintaining context across interactions and learning from past experiences\n- **Reflection**: Self-evaluation and course correction when things go wrong",
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  },
  {
    "text": "## The Agent Loop\n\nAt its core, most agentic systems follow a similar pattern:",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_2",
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  {
    "text": "```\n┌─────────────────────────────────────────┐\n│                                         │\n│   User Goal/Task                        │\n│         │                               │\n│         ▼                               │\n│   ┌───────────┐                         │\n│   │  Observe  │ ◄── Environment State   │\n│   └─────┬─────┘                         │\n│         │                               │\n│         ▼                               │\n│   ┌───────────┐                         │\n│   │   Think   │ ◄── LLM Reasoning       │\n│   └─────┬─────┘                         │\n│         │                               │\n│         ▼                               │\n│   ┌───────────┐                         │\n│   │    Act    │ ──► Tool Execution      │\n│   └─────┬─────┘                         │",
    "source": "agentic_architecture.md",
    "type": "markdown",
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  {
    "text": "│   ┌───────────┐                         │\n│   │    Act    │ ──► Tool Execution      │\n│   └─────┬─────┘                         │\n│         │                               │\n│         ▼                               │\n│   Goal Achieved? ──No──►  Loop Back     │\n│         │                               │\n│        Yes                              │\n│         │                               │\n│         ▼                               │\n│   Return Result                         │\n│                                         │\n└─────────────────────────────────────────┘\n```",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_4",
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  {
    "text": "This \"observe-think-act\" loop allows agents to iteratively work toward goals, adapting their approach based on feedback from each action.",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_5",
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  {
    "text": "## Key Concepts in Agentic Systems\n\n### Tools and Function Calling\n\nTools extend an agent's capabilities beyond text generation. Modern LLMs support \"function calling\" where the model can request to execute specific functions with structured parameters:\n\n```python\n# Example: Defining a tool for an agent\ntools = [\n    {\n        \"name\": \"search_database\",\n        \"description\": \"Search the product database for items\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"query\": {\"type\": \"string\", \"description\": \"Search query\"},\n                \"limit\": {\"type\": \"integer\", \"description\": \"Max results\"}\n            }\n        }\n    }\n]\n```",
    "source": "agentic_architecture.md",
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  {
    "text": "### Memory Systems\n\nAgents need memory to maintain coherence across long interactions:\n\n- **Short-term memory**: Current conversation context\n- **Long-term memory**: Persistent storage of facts, preferences, and past interactions\n- **Episodic memory**: Recollection of specific past events or tasks\n- **Semantic memory**: General knowledge and learned concepts\n\n### Planning Strategies\n\nDifferent approaches to breaking down complex tasks:\n\n- **ReAct (Reasoning + Acting)**: Interleave reasoning traces with actions\n- **Chain of Thought**: Step-by-step reasoning before acting\n- **Tree of Thoughts**: Explore multiple reasoning paths\n- **Plan-and-Execute**: Create a full plan first, then execute",
    "source": "agentic_architecture.md",
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  {
    "text": "## The Agentic Ecosystem\n\nThe field is rapidly evolving with frameworks, protocols, and tools designed to make building agents easier and more reliable.\n\n### Agent Frameworks\n\nSeveral frameworks have emerged to simplify agent development:\n\n**Pydantic AI** - A Python framework that leverages Pydantic's type system for building type-safe, validated agents. It emphasizes developer experience with clean APIs and strong typing.\n\n**AWS Bedrock Agents** - Amazon's managed service for building agents that can securely connect to enterprise data sources and take actions. Provides infrastructure for production-grade agent deployments.\n\n**LangGraph** - A framework for building stateful, multi-actor applications with LLMs, supporting complex agent workflows and coordination.",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_8",
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    "text": "### Interoperability Protocols\n\nAs agents become more sophisticated, standards for communication become crucial:\n\n**Model Context Protocol (MCP)** - An open protocol developed by Anthropic for connecting AI assistants to external data sources and tools. MCP provides a standardized way to expose capabilities to LLMs.\n\n**Agent2Agent (A2A)** - Google's protocol enabling different AI agents to communicate and collaborate, regardless of their underlying frameworks or vendors.\n\n### Observability and Monitoring\n\nProduction agents require robust monitoring:\n\n**Langfuse** - An open-source observability platform for LLM applications. It provides tracing, analytics, and evaluation tools specifically designed for understanding agent behavior and debugging issues.",
    "source": "agentic_architecture.md",
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    "text": "## Why Agentic Architecture Matters\n\n### Current Applications\n\nAgents are already transforming various domains:\n\n- **Coding Assistants**: Autonomous code generation, debugging, and refactoring\n- **Research Agents**: Literature review, data analysis, and report generation\n- **Customer Service**: Complex issue resolution with tool access\n- **Data Analysis**: End-to-end analysis from data collection to visualization\n- **Personal Assistants**: Calendar management, email handling, task automation",
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  {
    "text": "### Challenges and Considerations\n\nBuilding reliable agents comes with unique challenges:\n\n- **Reliability**: Ensuring consistent behavior across diverse inputs\n- **Safety**: Preventing harmful actions, especially with tool access\n- **Cost**: Managing token usage and API costs at scale\n- **Latency**: Balancing thoroughness with response time\n- **Evaluation**: Measuring agent performance on open-ended tasks",
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  {
    "text": "## Getting Started\n\nIf you're new to building agents, here's a recommended learning path:\n\n1. **Understand LLM fundamentals** - Function calling, prompting techniques\n2. **Start with a framework** - Pick one (Pydantic AI is great for Python devs)\n3. **Build a simple agent** - A single-tool agent for a specific task\n4. **Add complexity gradually** - Multiple tools, memory, planning\n5. **Implement observability** - Add Langfuse or similar from the start\n6. **Explore protocols** - Understand MCP and A2A for interoperability",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_12",
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  {
    "text": "## Topics to Explore\n\nThis introduction sets the stage for deeper dives into specific aspects of agentic architecture:\n\n- [Pydantic AI](pydantic-ai.html) - Type-safe agent development in Python\n- [AWS Bedrock Agents](bedrock-agents.html) - Enterprise-grade managed agents\n- [Model Context Protocol](mcp.html) - Standardized tool and data integration\n- [Agent2Agent Protocol](a2a.html) - Multi-agent communication\n- [Langfuse](langfuse.html) - Observability for LLM applications\n- [Agent Design Patterns](agent-patterns.html) - Common architectures and best practices",
    "source": "agentic_architecture.md",
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  {
    "text": "## Key Takeaways\n\n- Agentic systems extend LLMs with autonomy, tools, planning, and memory\n- The observe-think-act loop is fundamental to agent behavior\n- Multiple frameworks exist with different tradeoffs (Pydantic AI, Bedrock, etc.)\n- Protocols like MCP and A2A enable interoperability\n- Observability (Langfuse) is essential for production systems\n- Start simple and add complexity as needed",
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  {
    "text": "## Resources\n\n### Documentation\n- [Anthropic MCP Documentation](https://modelcontextprotocol.io/)\n- [Pydantic AI Docs](https://ai.pydantic.dev/)\n- [AWS Bedrock Agents](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html)\n- [Langfuse Documentation](https://langfuse.com/docs)\n\n### Papers and Research\n- \"ReAct: Synergizing Reasoning and Acting in Language Models\"\n- \"Toolformer: Language Models Can Teach Themselves to Use Tools\"\n- \"Generative Agents: Interactive Simulacra of Human Behavior\"\n\n---\n\n**Last Updated:** November 25, 2025  \n**Difficulty Level:** Intermediate",
    "source": "agentic_architecture.md",
    "type": "markdown",
    "chunk_id": "agentic_architecture.md_15",
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  {
    "text": "---\ntitle: \"Quantization 2\"\nid: \"quantization_2\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"quantization\", \"medium\"]\nrelated: [\"quantization\"]\ndate: \"2024-05-06\"\ndescription: \"Mastering Post-training Quantization: A Guide with PyTorch and TensorFlow\"\n---\n\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/quantization-2-7398a0ce7584) \n\nUnderstanding quantization can feel like learning two different languages when comparing TensorFlow and PyTorch. In this \npost, I’ll guide you through the differences and similarities, providing clear explanations along the way. You can find \nall the code references and notes in the provided [notebook](https://github.com/pelinbalci/LLM_Notebooks/blob/main/Quantization.ipynb)",
    "source": "2024-05-06-Quantization_2.md",
    "type": "markdown",
    "chunk_id": "2024-05-06-Quantization_2.md_0",
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  {
    "text": "---\ntitle: \"LLM Configurations\"\nid: \"configuration\"\ncategory: \"genai\"\ntags: [\"llm\", \"configuration\", \"top_k\", \"medium\", \"video\"]\nrelated: [\"llm\", \"temperature\"]\ndate: \"2023-07-22\"\ndescription: \"A brief introduction to LLM configurations\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/llm-inference-222c8e8a6ba7) \n\nWhat are the configuration parameters that can influence the model’s output during inference? \n\nClick here to watch the video on [YouTube](https://youtu.be/KbUPOJ8Fmzs)",
    "source": "2023-07-22-LLM_Configurations.md",
    "type": "markdown",
    "chunk_id": "2023-07-22-LLM_Configurations.md_0",
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  {
    "text": "---\ntitle: \"LLM Evaluation\"\nid: \"evaluation\"\ncategory: \"genai\"\ntags: [\"evaluation\", \"llm\", \"medium\"]\nrelated: [\"llm\"]\ndate: \"2023-08-01\"\ndescription: \"A practical guide to measure the model performance\"\n---\n\n\n[TL;DR? Read the full version on Medium](https://medium.com/@balci.pelin/llm-evaluation-6b63b3cfd48b) \n\nThis article is a practical guide to measure the model performance and to make sure that the model gives good results \nafter fine-tuning.",
    "source": "2023-08-01-LLM_Evaluation.md",
    "type": "markdown",
    "chunk_id": "2023-08-01-LLM_Evaluation.md_0",
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  {
    "text": "---\ntitle: \"Deep Dream Generator\"\nid: \"deepdream\"\ncategory: \"deep-learning\" # \"genai\", \"deep-learning\", \"conference\", \"machine-learning\", \"edge-ml\", \"visualization\"\ntags: [\"deep dream\", \"image generation\", \"image\", \"inception\", \"CNN\"]\nrelated: [\"imageprocess\"]\ndate: \"2026-01-09\"\ndescription: \"What happens when you ask a neural network to enhance what it sees, over and over again? You get Deep Dream—one of the first techniques that let us peek inside the black box of AI\"\n---\n\nI am reading the book How Smart Machines Think by Sean Gerrish. In one chapter, he explains the Deep Dream Generator. \nIt was very popular in 2015, and I used it a lot to make my pictures look dreamy. Thanks to Clade, I can now understand \nthe logic behind it. Here is a notebook that explains it better.",
    "source": "2026-01-09-Deep_Dream_Generator.md",
    "type": "markdown",
    "chunk_id": "2026-01-09-Deep_Dream_Generator.md_0",
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  {
    "text": "The Colab link: https://colab.research.google.com/drive/1EvQjVea0M3qkUVMkil3ZtujnGcRGjBB_?usp=sharing\n\nThe actual deep dream generator: https://deepdreamgenerator.com/\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/deep_dream_tutorial.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>",
    "source": "2026-01-09-Deep_Dream_Generator.md",
    "type": "markdown",
    "chunk_id": "2026-01-09-Deep_Dream_Generator.md_1",
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  {
    "text": "---\ntitle: \"LLM FewShot Learning\"\nid: \"fewshot\"\ncategory: \"genai\"\ntags: [\"llm\", \"fewshot\", \"zeroshot\", \"prompt\", \"medium\", \"video\"]\nrelated: [\"llm\"]\ndate: \"2023-07-24\"\ndescription: \"Explanation of Zero-Shot One-Shot and Few Shot on code\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/llm-few-shot-learning-d7df1d2c4446) \n\nExplanation of Zero-Shot One-Shot and Few Shot on code.\n\nClick here to watch the video on [YouTube](https://youtu.be/byRgzRCRSvw)",
    "source": "2023-07-24-LLM_FewShot_Learning.md",
    "type": "markdown",
    "chunk_id": "2023-07-24-LLM_FewShot_Learning.md_0",
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    "text": "---\ntitle: \"Arduino Edge Impulse Set Up\"\nid: \"arduino-edgeimpulse\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"arduino\"]\nrelated: [\"arduinosensor\"]\ndate: \"2021-11-22\"\ndescription: \"Set Arduino Tiny ML Kit up in Edge Impulse\"\n---\n\nI've bought Arduino Tiny Machine Learning Kit for my tiny machine learning projects. As a start, I'm taking the lecture: Introduction to Embedded Machine Learning from Coursera. To set the device up to Edge impulse you need to install Edge Impulse CLI and Arduino CLI. \nHere you can find some tips so that you don't deal with errors during the installation: \nRemember that those tips are valid for Windows OS. \n\n### 1. Sign up to edge impulse and create a project.",
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    "text": "### 2. Install Edge Impulse CLI: \n\n- [https://docs.edgeimpulse.com/docs/cli-installation](https://docs.edgeimpulse.com/docs/cli-installation)\n- Make sure that you **download  Visual Studio Community (using the \"Desktop development with C++\" workload).** \n- Download the latest version of python. \n- Install node.js from: https://nodejs.org/en/\n- **Install the additional tools when installing NodeJS.** \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_arduino_images/nodejs.PNG\" width=\"60%\">\n  <div class=\"figcaption\">Figure 1: Node.js download</div>\n</div>",
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  {
    "text": "- Check this website for additional configurations: [https://github.com/nodejs/node-gyp#on-windows](https://github.com/nodejs/node-gyp#on-windows)\n  \n- Set msvs version\n  \n    `$npm config set msvs_version 2017`\n\n- If you have multiple Python versions installed, **you need to identify which Python version node-gyp should use**: \n  [https://github.com/nodejs/node-gyp#on-windows]\n\n    `$npm config set python /path/to/executable/python`\n\n- Additional info can be found here: [https://github.com/Microsoft/nodejs-guidelines](https://github.com/Microsoft/nodejs-guidelines/blob/master/windows-environment.md#compiling-native-addon-modules)\n- Install CLI tools via: \n  \n    `$npm install -g edge-impulse-cli --force`",
    "source": "2021-11-22-Arduino-Edge-Impulse.md",
    "type": "markdown",
    "chunk_id": "2021-11-22-Arduino-Edge-Impulse.md_2",
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  {
    "text": "(Note that before python config and downloading visual studio community, I got plenty of errors during installation of CLI tools. )",
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  {
    "text": "### 3. Install Arduino CLI \n\n- Install Arduino CLI to your C:/ directory from [https://arduino.github.io/arduino-cli/0.20/installation/](https://arduino.github.io/arduino-cli/0.20/installation/). Then add the Arduino \n  CLI installation path to your PATH environment variable. \n- Run the following commands:\n  \n    `$ arduino-cli core list`\n  \n    `$ arduino-cli board list`\n\nFor me, those commands were both empty :( I will explain the solution in a minute.",
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  {
    "text": "### 4. Update the firmware\n- You need to download the latest Edge Impulse firmware and unzip the file: \n  [https://docs.edgeimpulse.com/](https://docs.edgeimpulse.com/docs/arduino-nano-33-ble-sense#2-update-the-firmware)\n- Open flash_windows.bat to flash the firmware. \n- Wait until it is completed. \n  \n  `Here, I got this error: Unknown FQBN: platform arduino:mbed is not installed`\n\nIf you don't care about this error and jump to the last step (like me!) with the command \n\n    $edge-impulse-daemon\n\nyou will get timeout error.  :)\n\n  `Failed to get info off device Timeout when waiting for >  (timeout: 5000) onConnected`\n\nLet's turn back to Step 3. All these errors are coming from the arduino:mbed installation.\nI searched for the solution from the following websites:",
    "source": "2021-11-22-Arduino-Edge-Impulse.md",
    "type": "markdown",
    "chunk_id": "2021-11-22-Arduino-Edge-Impulse.md_5",
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  {
    "text": "- [forum_egde_impulse_1](https://forum.edgeimpulse.com/t/incorrect-fqbn-error-installing-ei-firmware-for-arduino-nano-33ble-sense/1372/12)\n- [forum_egde_impulse_2](https://forum.edgeimpulse.com/t/arduino-cli-version/1332/6)\n- [forum_egde_impulse_3](https://forum.edgeimpulse.com/t/issue-uploading-edge-impulse-firmware-for-nano/1683/2)\n- [forum_egde_impulse_4](https://forum.arduino.cc/t/problem-uploading-to-new-board-arduino-nano-33-ble/685958) \n- [get_start_with_arduino_1](https://create.arduino.cc/projecthub/B45i/getting-started-with-arduino-cli-7652a5)\n- [get_start_with_arduino_1](https://docs.arduino.cc/software/ide-v1/tutorials/getting-started/cores/arduino-mbed_nano)",
    "source": "2021-11-22-Arduino-Edge-Impulse.md",
    "type": "markdown",
    "chunk_id": "2021-11-22-Arduino-Edge-Impulse.md_6",
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  {
    "text": "Here you can find what I did to solve this problem:\n- Download Arduino IDE. \n- Download: Tools/Board Manager/ Arduino Mbed OS Nano Boards\n\nI think it is not helpful since $arduino-cli core list is still empty!\n\n- Run the following command:\n  \n    `$ arduino-cli core install arduino:mbed --> This gets error.`\n  \n- Close the firewall and run the command above again. It works! \n- Don't forget to open the firewall. \n  \n    `$ arduino-cli core list`\n  \n    `$ arduino-cli board list`  are not empty!\n\nI updated the firmware without getting any errors (Step 4). \n\nThen, with the latest step `$edge-impulse-daemon`, I've achieved to load my device to edge impulse :)",
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    "text": "`Edge Impulse serial daemon v1.13.16\n    Endpoints:\n        Websocket: wss://remote-mgmt.edgeimpulse.com\n        API:       https://studio.edgeimpulse.com/v1\n        Ingestion: https://ingestion.edgeimpulse.com\n    \n    ? Which device do you want to connect to? COM3 (Microsoft)\n    [SER] Connecting to COM3\n    [SER] Serial is connected, trying to read config...\n    [SER] Retrieved configuration\n    [SER] Device is running AT command version 1.6.0\n    \n    ? To which project do you want to connect this device? Pelin / arduino_motion\n    Setting upload host in device... OK\n    Configuring remote management settings... OK\n    Configuring API key in device... OK\n    Configuring HMAC key in device... OK\n    [SER] Device is not connected to remote management API, will use daemon",
    "source": "2021-11-22-Arduino-Edge-Impulse.md",
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  {
    "text": "Configuring HMAC key in device... OK\n    [SER] Device is not connected to remote management API, will use daemon\n    [WS ] Connecting to wss://remote-mgmt.edgeimpulse.com\n    [WS ] Connected to wss://remote-mgmt.edgeimpulse.com\n    ? What name do you want to give this device? pelin's arduino\n    [WS ] Device \"pelin's arduino\" is now connected to project \"arduino_motion\"\n    [WS ] Go to https://studio.edgeimpulse.com/studio/62428/acquisition/training to build your machine learning model!`",
    "source": "2021-11-22-Arduino-Edge-Impulse.md",
    "type": "markdown",
    "chunk_id": "2021-11-22-Arduino-Edge-Impulse.md_9",
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    "text": "<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_arduino_images/device.PNG\" width=\"90%\">\n  <div class=\"figcaption\">Figure 2: Finally! :) </div>\n</div>",
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  {
    "text": "## Key Words\nTiny ML, Arduino, Edge Impulse, Timeout, arduino:mbed",
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  {
    "text": "---\ntitle: \"Audio Classifier with Edge Impulse\"\nid: \"edge-audio\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"arduino\", \"audio\", \"classification\", \"tensorflow\"]\nrelated: [\"edge-motion\"]\ndate: \"2021-01-01\"\ndescription: \"A motion classifier project with Edge Impulse & Arduino\"\n---\n\n\n\nIn this post you can find my second project on Edge Impulse with Arduino Tiny ML kit. I used audio dataset this time. \n\nI would like to thank Coursera and Edge Impulse to give us a chance to learn embedded machine learning. \nFind more about the lecture: [Coursera](https://www.coursera.org/learn/introduction-to-embedded-machine-learning)",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_0",
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    "text": "In the project description two audio datasets are given, faucet and noise. I added blender audio to this dataset. \nI've run the blender for 2 minutes. Then using Audacity interface, I divided the 2 minute-data to 50 1-second data. \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/audacity.png\" width=\"80%\">\n  <div class=\"figcaption\">Audacity Interface </div>\n</div>\n\n--\n\nRest of the steps are:\n\n- Upload the data\n- Create Impulse\n- Generate Features\n- Model Training\n- Model Testing\n\nI've tried two different feature extraction method. I will explain the details of them later in the post.",
    "source": "2022-01-17-Audio-Classifier.md",
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  {
    "text": "### Deployment to Arduino\n\nOn the deployment page, select Arduino and click build. ei-audio_classifier-arduino-1.0.3.zip file is downloaded.\n\nSince I didn't collect the data with Arduino, this is the first time I connected Arduino to the computer. \n\nDon't forget to update the firmware: run flash_windows. After the firmware is updated open the command prompt:\n\n    $edge-impulse-daemon --clean.\n\nI reentered my credentials and selected my current project. \n\nSmile :)\n\n    [WS ] Device \"myarduino\" is now connected to project \"audio_classifier\"\n    [WS ] Go to https://studio.edgeimpulse.com/studio/75736/acquisition/training to build your machine learning model!\n\nNow it is time to use Arduino IDE. From Sketch -> Add Zip Library, select ei-audio_classifier-arduino-1.0.3.zip",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
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  {
    "text": "Now it is time to use Arduino IDE. From Sketch -> Add Zip Library, select ei-audio_classifier-arduino-1.0.3.zip\n\nSelect File -> Examples --> audio-classifier-inferencing- nano_ble33_sense_microphone_continuous \n\nHere comes the final step: I clicked \"Upload\" to compile and send the program to Arduino. Unfortunately, I got this error:\n\n    Error sample buffer overrun. Decrease the number of slices per model window (EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)\n    ERR: Failed to record audio...",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_3",
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    "text": "I decreased the EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW from 3 to 2 by applying the directions on the website. \nRead more about this problem from: [edge_impulse](https://docs.edgeimpulse.com/docs/continuous-audio-sampling)\n    \n    /**\n     * Define the number of slices per model window. E.g. a model window of 1000 ms\n     * with slices per model window set to 4. Results in a slice size of 250 ms.\n     * For more info: https://docs.edgeimpulse.com/docs/continuous-audio-sampling\n     */\n    # define EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW 2\n\n:)\n\nI run the blender, and the result is below :)\nThat's great!",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_4",
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  {
    "text": ":)\n\nI run the blender, and the result is below :)\nThat's great!\n\n\n    Predictions (DSP: 165 ms., Classification: 253 ms., Anomaly: 0 ms.): \n        blender: 0.00000\n        faucet: 0.00000\n        noise: 0.99609\n    Predictions (DSP: 165 ms., Classification: 253 ms., Anomaly: 0 ms.): \n        blender: 0.00000\n        faucet: 0.00000\n        noise: 0.99609\n    Predictions (DSP: 165 ms., Classification: 253 ms., Anomaly: 0 ms.): \n        blender: 0.62695\n        faucet: 0.00000\n        noise: 0.37305\n    Predictions (DSP: 165 ms., Classification: 253 ms., Anomaly: 0 ms.): \n        blender: 0.94531\n        faucet: 0.00000\n        noise: 0.05469",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_5",
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  {
    "text": "## Feature Extraction\n\nI got better results with MFE Feature Extraction. I used 2d conv net for MFE, while I used 1DConv for MFFC.\n\nMFCCs are commonly derived as follows:\n- Take the Fourier transform of (a windowed excerpt of) a signal.\n- Map the powers of the spectrum obtained above onto the mel scale, using triangular overlapping windows or alternatively, cosine overlapping windows.\n- Take the logs of the powers at each of the mel frequencies. \n- Take the discrete cosine transform of the list of mel log powers, as if it were a signal.\n- The MFCCs are the amplitudes of the resulting spectrum. [wikipedia](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum)\n\nMFE:",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_6",
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  {
    "text": "MFE:\n\nThe Mel-scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another. \nThe idea is to extract more features (more filter banks) in the lower frequencies, \nand less in the high frequencies, thus it performs well on sounds that can be distinguished by human ear. [edge_impulse](https://docs.edgeimpulse.com/docs/audio-mfe)\n\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/mfcc_mfe_1.PNG\">\n  <div class=\"figcaption\">DSP </div>\n</div>\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/mfcc_mfe_2.PNG\">\n  <div class=\"figcaption\">Parameters </div>\n</div>\n\n--",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
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  {
    "text": "--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/mfcc_mfe_3.PNG\" width=\"80%\">\n  <div class=\"figcaption\">MFE Separates better </div>\n</div>\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/mfcc_mfe_4.PNG\">\n  <div class=\"figcaption\">Training Accuracy </div>\n</div>\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_audio_images/mfcc_mfe_4.PNG\">\n  <div class=\"figcaption\">Test Accuracy </div>\n</div>",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_8",
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  {
    "text": "### Key Words\nTiny ML, Arduino, Edge Impulse, Audio, Classification",
    "source": "2022-01-17-Audio-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-17-Audio-Classifier.md_9",
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  {
    "text": "---\ntitle: \"AWS re:Invent Recap 1\"\nid: \"reinvent\"\ncategory: \"conference\"\ntags: [\"aws\", \"reinvent\", \"medium\"]\nrelated: [\"reinvent_2\"]\ndate: \"2024-06-05\"\ndescription: \"re:Invent notes\"\n---\n\n\n[TL;DR? Read the full version on Medium](https://medium.com/@balci.pelin/aws-re-invent-2024-notes-part1-f185ed9b326b) \nThis year, I was privileged to join the AWS re: Invent in Las Vegas! It was a wonderful week, I met incredible people \nfrom around the world, exchanging ideas, and learning from their unique experiences. I’ve decided to write and share my notes :)",
    "source": "2024-12-17-aws-reinvent.md",
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  {
    "text": "---\ntitle: \"EasyOCR in HuggingFace\"\nid: \"easyocr\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"image\", \"medium\", \"huggingface\"]\nrelated: [\"imageprocess\"]\ndate: \"2023-03-08\"\ndescription: \"Create a space about EasyOCR in HuggingFace\"\n---\n\n\n[TL;DR? Read the full version on Medium](https://medium.com/@balci.pelin/easyocr-with-hugging-face-with-streamlit-f5a27666ec5c) \n\nYou can start creating your apps on Hugging Face platform today!",
    "source": "2023-03-08-EasyOCR_HF_Streamlit.md",
    "type": "markdown",
    "chunk_id": "2023-03-08-EasyOCR_HF_Streamlit.md_0",
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  {
    "text": "---\ntitle: \"AWS re:Invent Recap 2\"\nid: \"reinvent_2\"\ncategory: \"conference\"\ntags: [\"aws\", \"reinvent\", \"medium\"]\nrelated: [\"reinvent\"]\ndate: \"2024-06-05\"\ndescription: \"re:Invent notes\"\n---\n\n\n[TL;DR? Read the full version on Medium](https://medium.com/@balci.pelin/aws-re-invent-2024-notes-part2-5318a2295bf9) \nWelcome to the second part of my AWS re: Invent series! In this post, I’ll be diving into some of the exciting announcements and sharing insights from the sessions I attended.",
    "source": "2024-12-18-aws-reinvent-2.md",
    "type": "markdown",
    "chunk_id": "2024-12-18-aws-reinvent-2.md_0",
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  {
    "text": "---\ntitle: \"Style Transfer\"\nid: \"styletransfer\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"image\"]\nrelated: [\"imageprocess\"]\ndate: \"2020-10-20\"\ndescription: \"How to transfer the style of an image to another image using CNN\"\n---\n\nIn this post, I would like to share my notes from the 'Style Transfer' lecture given by Udacity, Intro to Deep Learning \nwith PyTorch. You may find the references below. \n\nThe key is using the technique that a trained CNN to separate the content from the style of an image. \nThen you can merge the content of one image and style of another and you will get a new image.  \n\n    content image + style image = target image\n\n**Separating Style & Content**",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
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  {
    "text": "content image + style image = target image\n\n**Separating Style & Content**\n\nIn deep networks, the image is transformed and the layers care about the content of the image, rather than texture and \ncolors. But what about style? It is texture, colors, curvature...",
    "source": "2020-10-20-style-transfer.md",
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  {
    "text": "## VGG19 Paper Summary\n\nIn this paper[1]  style transfer uses the features found in 19 layer VGG network. \n\n- Input: a color image.\n- Pass through conv and pool layers. \n- Finally 3 FC layers. Classify the image.\n- In btw 5 pooling layers --> 2 or 4 conv layers.\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/image_op/vgg_fig.png\" width=\"75%\">\n  <div class=\"figcaption\">Figure 1: VGG Layers for Style and Content Images</div>\n</div>",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_2",
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  {
    "text": "### Explanation of the Figure:\n\nThis is an image representations in a Convolutional Neural Network (CNN). \n\nA given input image is represented as a set of filtered images at each processing stage in the CNN. While the number of \ndifferent filters increases along the processing hierarchy, the size of the filtered\nimages is reduced by some down sampling mechanism (e.g. max-pooling) leading to a decrease in the total number of units \nper layer of the network. \n\n#### Content Reconstructions:\n\nWe can visualise the information at different processing stages in the CNN by reconstructing the input image from only \nknowing the network’s responses in a particular layer.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_3",
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    "text": "We reconstruct the input image from from layers:\n- ‘conv1 2’ (a),\n- ‘conv2 2’ (b), \n- ‘conv3 2’ (c), \n- ‘conv4 2’ (d)\n- ‘conv5 2’ (e) of the original VGG-Network. \n\nWe find that reconstruction from lower layers is almost perfect (a–c). In higher layers of the network, detailed pixel \ninformation is lost while the high-level content of the image is preserved (d,e). \n\n#### Style Reconstructions",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_4",
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  {
    "text": "#### Style Reconstructions\n\nOn top of the original CNN activations we use a feature space that captures the texture information of an\ninput image. The style representation computes correlations between the different features in different layers of the CNN. \nWe reconstruct the style of the input image from a style representation built on different subsets of CNN layers \n- ‘conv1 1’ (a), \n- ‘conv1 1’ and ‘conv2 1’ (b), \n- ‘conv1 1’, ‘conv2 1’ and ‘conv3 1’ (c), \n- ‘conv1 1’, ‘conv2 1’, ‘conv3 1’ and ‘conv4 1’ (d), \n- ‘conv1 1’, ‘conv2 1’, ‘conv3 1’, ‘conv4 1’ and ‘conv5 1’ (e). \n\nThis creates images that match the style of a given image on an increasing scale while discarding information of the\nglobal arrangement of the scene.\" [1]",
    "source": "2020-10-20-style-transfer.md",
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  {
    "text": "## Content\n\n- Conv_Layer output is the content representation of the input. \n- Next when it sees the style image it will extract features from multiple layers that represent the style of the image. \n\nTarget image can be started from blank canvas or copy of the content image. Target image's content should be close to content image and\nits style should be close to the style image. \n\nWe need to compare content representation od content image and content representation of target image. \n\nHow? \n\n**Content Loss**\n \n    Tc: target content rep, Cc: content content rep\n    CONTENT LOSS = 1/2 * Sum (Tc - Cc)^2\n    \nThe only value that change is Tc. We are not training CNN, but we are using backpropagation to min the loss function.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_6",
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    "text": "## Style\n\nWe need to calculate the difference btw target image and style image. We\nfind the similarities btw features in multiple layers in the network. \n\n- Include correlations btw multiple layers of different sizes\n- we can obtain multiscale style representation of te input image. \n\n**Style representation calculation:**\n\nIt is calculated by using all conv layers first channel: conv1_1, conv2_1 ...\nThe correlations at each layer are given by a GRAM MATRIX.\n \n**GRAM MATRIX**\n\n- Input image 4*4\n- Use 8 filters for conv layer\n- Output: 4*4 in height and weight and 8 in depth. --> It has 8 features maps that we want to find relationships between.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
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    "text": "Operations:\n- Vectorize the values in this layer: 8 * 4 * 4 -> 4*4=16 (one feature map): `8*16`\n- Take the transpose of this matrix : `8*16`\n- Multiply these two matrices: `8*8`\n\nResult of the gram matrix contains non localized information about the layer. \n\n**What is nonlocalized info??**\n\nIt is information that would still be there even if an image was shuffled around in space. Even if the content of a filtered image\nis not identifable, you should be able to see the prominent colors and textures ie. the style!\n\nMatrix(8 * 16) * Matrix(16 * 8) = Gram Matrix(8 * 8) --> its values indicates the similarities btw the layers.  \n\n- Ref: https://en.wikipedia.org/wiki/Gramian_matrix",
    "source": "2020-10-20-style-transfer.md",
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  {
    "text": "- Ref: https://en.wikipedia.org/wiki/Gramian_matrix\n\nAn important application is to compute linear independence: a set of vectors are linearly independent if and only if the\nGram determinant (the determinant of the Gram matrix) is non-zero.\n\n- Ref: https://stackoverflow.com/questions/55546873/how-do-i-flatten-a-tensor-in-pytorch\n\nHow to reshape the tensor?\n\n    import torch\n    input = torch.rand(3, 8, 8)\n    print(input.shape) # torch.Size([3, 8, 8])\n    print(input.flatten(start_dim=1, end_dim=2).shape) # torch.Size([3, 64])\n    reshape_input  = input.flatten(start_dim=1, end_dim=2)\n    reshape_input.shape # torch.Size([3, 64])",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_9",
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    "text": "How to reshape the tensor? --> In the solution\n    \n    # get the batch_size, depth, height, and width of the Tensor\n    _, d, h, w = tensor.size()\n    \n    # reshape so we're multiplying the features for each channel\n    tensor = tensor.view(d, h * w)\n    \n    # calculate the gram matrix\n    gram = torch.mm(tensor, tensor.t())\n    \n\n**Style Loss**\n\n- Ss = Style image style\n- Ts = Target image style\n\nLoss = a * Sum(wi (Tsi - Ssi)^2  --> a is a constant that accounts for the number of values in each layer.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_10",
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  {
    "text": "## Loss\n\nTotal Loss =Loss_content + Loss_style\n\nBy using backpropagation we change the target image to minimize the loss.\n\nSince these two losses can have very different values: (0.1 + 102) we need to treat them equally:\n\nTotal Loss = &alpha; * Loss_content + &Beta; * Loss_style\n\n**Effect of Alpha & Beta Ratio:**\n\n- &alpha; / &Beta; = 1/10 ---> more content + less style (we can see the shape pf house clearly)\n- &alpha; / &Beta; = 1/10000 ---> less content + more style (we loose the shape of house)\n\nThe more small &alpha; / &Beta; ratio the more style you see!",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_11",
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  },
  {
    "text": "## Coding Tips\n\n- Load VGG features, not classification part\n        \n        \n        vgg = models.vgg19(pretrained=True).features\n        \n- Freeze all VGG parameters since we're only optimizing the target image\n\n        \n        for param in vgg.parameters():\n            param.requires_grad_(False)\n    \n- Features of Vgg19:",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_12",
    "embedding": [
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  {
    "text": "(20): ReLU(inplace=True)\n      (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (22): ReLU(inplace=True)\n      (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (24): ReLU(inplace=True)\n      (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (26): ReLU(inplace=True)\n      (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n      (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (29): ReLU(inplace=True)\n      (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (31): ReLU(inplace=True)\n      (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (33): ReLU(inplace=True)",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_15",
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    "text": "(31): ReLU(inplace=True)\n      (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (33): ReLU(inplace=True)\n      (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (35): ReLU(inplace=True)\n      (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n    )",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
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  {
    "text": "- Classifier of Vgg19:\n\n\n            Sequential(\n      (0): Linear(in_features=25088, out_features=4096, bias=True)\n      (1): ReLU(inplace=True)\n      (2): Dropout(p=0.5, inplace=False)\n      (3): Linear(in_features=4096, out_features=4096, bias=True)\n      (4): ReLU(inplace=True)\n      (5): Dropout(p=0.5, inplace=False)\n      (6): Linear(in_features=4096, out_features=1000, bias=True)\n    )\n        \n- How to use cuda?\n\n\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        vgg.to(device)   \n\n- Load image with image path\n\n\n        image = Image.open(img_path).convert('RGB')\n\n- Use tranformation to resize normalize and turn to tensor:",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_17",
    "embedding": [
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  {
    "text": "- Load image with image path\n\n\n        image = Image.open(img_path).convert('RGB')\n\n- Use tranformation to resize normalize and turn to tensor:\n\n\n        transforms.Compose([\n                        transforms.Resize(size),\n                        transforms.ToTensor(),\n                        transforms.Normalize((0.485, 0.456, 0.406), \n                                             (0.229, 0.224, 0.225))])\n\n          \n        # discard the transparent, alpha channel (that's the :3) and add the batch dimension\n        image = in_transform(image)[:3,:,:].unsqueeze(0)\n        \n        \n- Load both style and content image. They may have different shapes. Use content image hegiht and weight for style image.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_18",
    "embedding": [
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  },
  {
    "text": "style = load_image(path, shape=content.shape[-2:]).to(device)\n        \n- Since we turn the image to tensor:\n\n\n        print(content)\n        \n        tensor([[[[-1.8953, -1.9467, -1.9295,  ..., -0.1999, -0.1999, -0.3027],\n          [-1.9980, -2.0152, -1.9467,  ..., -0.4568, -0.3369, -0.1657],\n          [-1.9809, -1.9809, -1.9295,  ..., -0.8164, -0.5596, -0.3027],\n          ...,\n                \n- If you want to see the image you should be turn it to numpy array, transpose, normalize it and clip the values, \nthen use plt.imshow(image) to see the image.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_19",
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  },
  {
    "text": "image = tensor.to(\"cpu\").clone().detach()\n        image = image.numpy().squeeze()\n        image = image.transpose(1,2,0)\n        image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\n        image = image.clip(0, 1)\n             \n  \n- Select the layers you would like to use: {'0': conv1_1, '3': conv2_1 ...}\n\n- Take the input image.  pass through each layer, store the output of the layer. If this layer is one of out layers, \nstore the output in features dictionary.",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_20",
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  {
    "text": "vgg._modules.items()\n        odict_items([('0', Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('1', ReLU(inplace=True)), \n        \n        name, layer in vgg._modules.items():\n        \n        name = '0'\n        layer = Conv2d(3, 64, kernel_size=(3, 3)\n        layers['0']= conv1_1 \n        \n        output = layer(input image)\n        \n        features = {layers['0']: [output..]}\n        features = {'conv1_1': [output..]}\n        \n- You can calculate, target, content and style features. \n\n- Calculate gram matrix",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_21",
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  {
    "text": "- Calculate gram matrix\n\n\n       # get the batch_size, depth, height, and width of the Tensor\n       _, d, h, w = tensor.size()\n    \n        # reshape so we're multiplying the features for each channel\n        tensor = tensor.view(d, h * w)\n        \n        # calculate the gram matrix\n        gram = torch.mm(tensor, tensor.t())\n        \n- Calculate the gram matrices for each layer of our style representation\n\n\n- Create target image by cloning the content image and it require gradient\n\n\n        target = content.clone().requires_grad_(True).to(device)\n\n- Assign weights to selected style layers. Assign alpha and beta.\n        \n        \n         weighting earlier layers more will result in *larger* style artifacts\n\n- Use optimizer = optim.Adam([target], lr=0.003)",
    "source": "2020-10-20-style-transfer.md",
    "type": "markdown",
    "chunk_id": "2020-10-20-style-transfer.md_22",
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    "text": "- Use optimizer = optim.Adam([target], lr=0.003)\n\n- Decide the number of iterations. It may be 2000 or 5000\n\n- content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)\n\n- For each layer in layers:\n\n    - get the features (output) of target and style\n    - calculate gram matrix\n    - layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram)**2)\n    - style_loss += layer_style_loss / (d * h * w)\n    \n- Total Loss: content_weight * content_loss + style_weight * style_loss\n\n- Optimization part:\n\n        optimizer.zero_grad()\n        total_loss.backward()\n        optimizer.step()",
    "source": "2020-10-20-style-transfer.md",
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  {
    "text": "## References\n\n- [Udacity_Lecture](https://classroom.udacity.com/courses/ud188/lessons/c1541fd7-e6ec-4177-a5b1-c06f1ce09dd8/concepts/e9180da9-001a-426e-974a-0ef1e87846dc)\n- [Gatys_Image_Style_Transfer_CVPR_2016_paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) [1]\n- [Code](https://github.com/udacity/deep-learning-v2-pytorch/blob/master/style-transfer/Style_Transfer_Solution.ipynb)\n\n\n## Key Words\n\n- Pre-trained VGG19, Style Transfer, Gram Matrix, PyTorch, Style Transfer Loss",
    "source": "2020-10-20-style-transfer.md",
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  {
    "text": "---\ntitle: \"Cross-Entropy Loss\"\nid: \"crossentropy\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"loss\", \"cross entropy\", \"medium\"]\nrelated: [\"dl\"]\ndate: \"2023-03-11\"\ndescription: \"Simple example for cross entropy loss\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/cross-entropy-loss-5568329c9d3) \n\nI will explain the cross-entropy loss with a very simple example.",
    "source": "2023-03-11-Cross_Entropy_Loss.md",
    "type": "markdown",
    "chunk_id": "2023-03-11-Cross_Entropy_Loss.md_0",
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  {
    "text": "---\ntitle: \"GDG Devfest'24 Notes\"\nid: \"devfest\"\ncategory: \"conference\"\ntags: [\"llmops\", \"google\", \"devfest\"]\nrelated: [\"reinvent\"]\ndate: \"2024-11-09\"\ndescription: \"I've joined GDG devfest'24 event.\"\n---\n\nI've joined GDG devfest'24 event yesterday. There were remarkable speakers. I would like to add my notes.\nYou may find the link of the conference [here](https://devfest.istanbul/)",
    "source": "2024-11-09-devfest_notes.md",
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  {
    "text": "## Devops MLOps LLMOps\n\nI've combined the presentation notes and get a little help from chatgpt to prepare this chart. It shows the differences\nbetween Devops, MLOps and LLMOps Practices. The presentations are: \n\n- From Devops to MLOps fro Majd Jamaah - Beyond Limits, GDE\n- From Ideation to production: GENAI Application  Development and LLMOps from Emrah Mete - Microsoft\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/devops_images/devops_mlops_llmops.png\" width=\"50%\">\n  <div class=\"figcaption\"> </div>\n</div>",
    "source": "2024-11-09-devfest_notes.md",
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  {
    "text": "## Useful Links\n\nGoogle Trainings: https://cloud.google.com/learn/training/machinelearning-ai\n\nAlpha Proteo: https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/\n\nNobel Prize: https://deepmind.google/discover/blog/demis-hassabis-john-jumper-awarded-nobel-prize-in-chemistry/\n\nGemini Long Context Kaggle Competition: https://www.kaggle.com/competitions/gemini-long-context\n\nAgents:\n- https://haystack.deepset.ai/cookbook/web_enhanced_self_reflecting_agent?utm_campaign=developer-relations&utm_source=devfest-ist-2024&utm_medium=presentation\n- https://github.com/daronyondem/codesamples/tree/main/AutoGen\n- https://lilianweng.github.io/posts/2023-06-23-agent/",
    "source": "2024-11-09-devfest_notes.md",
    "type": "markdown",
    "chunk_id": "2024-11-09-devfest_notes.md_2",
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  {
    "text": "Run Language Models: \n- https://ollama.com/\n- https://lmstudio.ai/\n- https://www.microsoft.com/en-us/research/blog/introducing-autogen-studio-a-low-code-interface-for-building-multi-agent-workflows/",
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  {
    "text": "---\ntitle: \"LLM Finetuning\"\nid: \"finetuning\"\ncategory: \"genai\"\ntags: [\"evaluation\", \"llm\", \"medium\", \"finetuning\"]\nrelated: [\"llm\", \"evaluation\"]\ndate: \"2023-09-17\"\ndescription: \"A practical guide to measure the model performance\"\n---\n\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/llm-finetuning-410e8a2738ef) \n\nFinetuning doesn’t have \nto be a mystery anymore! In this article, I’ve created a simple notebook that breaks down the \nprocess in an easy-to-understand way.",
    "source": "2023-09-17-LLM_Finetuning.md",
    "type": "markdown",
    "chunk_id": "2023-09-17-LLM_Finetuning.md_0",
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  {
    "text": "---\ntitle: \"Line Chart with Plotly\"\nid: \"chart\"\ncategory: \"visualization\"\ntags: [\"python\", \"visualization\"]\nrelated: [\"python\"]\ndate: \"2025-11-08\"\ndescription: \"How to make scatter plots in Python with Plotly.\"\n---\n\nIn this file I will explain how to use plotly with markers and annotation. It is very easy to plot chart in plotly express!:)\n\nLet's create two datasets: they include sales & anomalies for the first 10 days of Jan 2023.",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_0",
    "embedding": [
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  {
    "text": "Let's create two datasets: they include sales & anomalies for the first 10 days of Jan 2023. \n\n```python\n# create dataset\nimport pandas as pd\ndf = pd.DataFrame({\"Date\": [\"2023-01-01\", \"2023-01-02\", \"2023-01-03\", \"2023-01-04\", \"2023-01-05\", \"2023-01-06\", \"2023-01-07\", \n              \"2023-01-08\", \"2023-01-09\", \"2023-01-10\"],\n     \"Analysis\": [\"Normal\", \"Anomaly\", \"Normal\", \"Anomaly\", \"Anomaly\", \"Normal\", \"Normal\", \"Normal\", \"Normal\", \"Normal\"],\n     \"Sales\": [120, 30, 115, 10, 5, 100, 99, 123, 134, 96]})\n\n```\n\nWe need to convert the \"date\" to datetime to show it in x axis properly. \n\n```\n# to datetime\ndf.Date = pd.to_datetime(df.Date)\ndf_with_group.Date = pd.to_datetime(df_with_group.Date)\n```\n\nHere is a simple way to plot a animation chart!",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_1",
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  {
    "text": "Here is a simple way to plot a animation chart!\n\n```python\nimport plotly.express as px\nfig = px.line(df, x=\"Date\", y=\"Sales\", title='Daily Sales')\nfig.show()\n```\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/plotly_simple.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>\n\n\n\nNow it is time to mark the anomalies! We will use Graph objects for this. Start with mark the anomaly dates:\n\n```python\nanomaly_date = df[df.Analysis == \"Anomaly\"]\n```\n\nIt is required to import graph objects to mark \"anomalies\".",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_2",
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  },
  {
    "text": "```python\nanomaly_date = df[df.Analysis == \"Anomaly\"]\n```\n\nIt is required to import graph objects to mark \"anomalies\".\n\n```python\nimport plotly.graph_objects as go\nfig = px.line(df, x=\"Date\", y=\"Sales\", title='Daily Sales')\nfig.add_traces(go.Scatter(x=anomaly_date[\"Date\"], y=anomaly_date[\"Sales\"], mode=\"markers\", name=\"Anomaly\", \n                          hoverinfo=\"skip\"))\nfig.show(renderer='notebook')\n```\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/plotly_mark.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>\n\nIf you want to see the \"Anomaly\" as a string on the chart, you should use \"add_annotation\":",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_3",
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  },
  {
    "text": "```python\nfig = px.line(df, x=\"Date\", y=\"Sales\", title='Daily Sales')\nfig.add_traces(go.Scatter(x=anomaly_date[\"Date\"], y=anomaly_date[\"Sales\"], mode=\"markers\", name=\"Anomaly\", \n                          hoverinfo=\"skip\"))\nfor idx in range(len(anomaly_date)):\n     fig.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),\n                                        x=anomaly_date[\"Date\"].iloc[idx],\n                                        y=anomaly_date[\"Sales\"].iloc[idx],\n                                        showarrow=False,\n                                        text=anomaly_date[\"Analysis\"].iloc[idx],\n                                        textangle=0,\n                                        xanchor='auto',  #['auto', 'left', 'center', 'right']",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_4",
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    "text": "textangle=0,\n                                        xanchor='auto',  #['auto', 'left', 'center', 'right']\n                                        xref=\"x\",\n                                        yref=\"y\"))\nfig.show()\n```",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
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  },
  {
    "text": "### How to embed plotly chart to md file?\n\nIt is a bit tricky when you want to publish your plotly express plots as md file. I prefer\nto convert my jupyter notebooks to html and publish them directly. However, when it comes to publish plotly there are many erros \nin github deployment. For example: Liquid syntax error\n\nI used jupyter notebook to create the plots and save the charts. Following function is used for saving plotly express as html file:\n\n```python\nimport plotly.express as px\nimport plotly.io as pio\nfig = px.line(df, x=\"Date\", y=\"Sales\", title='Daily Sales')\nfig.show()\npio.write_html(fig, file='plotly_simple.html', auto_open=False)\n```\n\nThen you can add the html in .md file:",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_7",
    "embedding": [
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  {
    "text": "Then you can add the html in .md file: \n\n    <div>\n    <iframe \n        src=\"https://www.pelinbalci.com/assets/components/plotly_mark_annotation.html\" \n        width=\"100%\" \n        height=\"600\" \n        style=\"border:none;\"\n    ></iframe>\n    </div>\n\nHappy learning! :)\n\n# References\n- https://raw.githubusercontent.com/plotly/plotly.py/master/doc/python/line-and-scatter.md\n- https://stackoverflow.com/questions/60513164/display-interactive-plotly-chart-html-file-on-github-pages\n- https://stackoverflow.com/questions/64241461/plotly-how-to-add-markers-at-specific-points-in-plotly-line-graph-python-pan\n- https://stackoverflow.com/questions/68731627/plotly-express-line-chart-mark-specific-points-and-retain-hover-data\n- https://davistownsend.github.io/blog/PlotlyBloggingTutorial/",
    "source": "2023-01-29-Line_Chart_with_Plotly.md",
    "type": "markdown",
    "chunk_id": "2023-01-29-Line_Chart_with_Plotly.md_8",
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  {
    "text": "---\ntitle: \"Image Processing\"\nid: \"imageprocess\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"image\"]\nrelated: [\"dl\"]\ndate: \"2020-10-19\"\ndescription: \"Loading an image with PIL and processing it with PyTorch and Numpy\"\n---\n\nIn this notebook, I will explain how to load an image with PIL and explain some operations with PyTorch and Numpy. \nThese processes are part of Style Transfer in Convolutional Neural Networks, there are two images: content and style. \nThe next post defines how to transfer the style of one image to the content of the other image.\n\nThis document is prepared by using Google Colab and the lecture notes from: https://classroom.udacity.com/courses/ud188\n\n**First of all; make sure that you are in the right content in drive.**",
    "source": "2020-10-19-image-operations.md",
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  {
    "text": "**First of all; make sure that you are in the right content in drive.**\n\n\n```python\nfrom google.colab import drive\ndrive.mount('/content/drive')\n```\n\n    Mounted at /content/drive\n\n\nUse GPU if available\n\n\n```python\n# move the model to GPU, if available\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n```\n\nImport libraries\n\n\n```python\n# import resources\n%matplotlib inline\n\nfrom PIL import Image\nfrom io import BytesIO\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport torch\nimport requests\nfrom torchvision import transforms, models\n```\n\n**Load Image**",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_1",
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    "text": "import torch\nimport requests\nfrom torchvision import transforms, models\n```\n\n**Load Image**\n\nThe function below is useful for loading images. \n- img_path should be the path the image in your drive. \n- Since the large images slow down the process, max_size is defined as 400. \n- transforms from torchvision i used to crop, resize, normalize the image and we can turn it to tensor. \n\n\nSimply you can use following code: \n```\nimage = Image.open(img_path).convert('RGB')\n```",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_2",
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  {
    "text": "```python\ndef load_image(img_path, max_size=400, shape=None):\n    ''' Load in and transform an image, making sure the image\n       is <= 400 pixels in the x-y dims.'''\n    if \"http\" in img_path:\n        response = requests.get(img_path)\n        image = Image.open(BytesIO(response.content)).convert('RGB')\n    else:\n        image = Image.open(img_path).convert('RGB')\n    \n    # large images will slow down processing\n    if max(image.size) > max_size:\n        size = max_size\n    else:\n        size = max(image.size)\n    \n    if shape is not None:\n        size = shape\n        \n    in_transform = transforms.Compose([\n                        transforms.Resize(size),\n                        transforms.ToTensor(),\n                        transforms.Normalize((0.485, 0.456, 0.406),",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
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    "text": "transforms.ToTensor(),\n                        transforms.Normalize((0.485, 0.456, 0.406), \n                                             (0.229, 0.224, 0.225))])",
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    "text": "# discard the transparent, alpha channel (that's the :3) and add the batch dimension\n    image = in_transform(image)[:3,:,:].unsqueeze(0)\n    \n    return image\n```\n\nLoad images by file name\n\n\n```python\ncontent = load_image('/content/drive/My Drive/Style_Transfer/images/octopus.jpg').to(device)\nstyle = load_image('/content/drive/My Drive/Style_Transfer/images/hockney.jpg').to(device)\n\nprint(content.shape)\nprint(style.shape)\n```\n\n    torch.Size([1, 3, 400, 592])\n    torch.Size([1, 3, 360, 263])\n\n\nWhat does it look like? \n\n\n```python\ncontent[0][0]\n```",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
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  {
    "text": "torch.Size([1, 3, 400, 592])\n    torch.Size([1, 3, 360, 263])\n\n\nWhat does it look like? \n\n\n```python\ncontent[0][0]\n```\n\n\n\n\n    tensor([[-1.8953, -1.9467, -1.9295,  ..., -0.1999, -0.1999, -0.3027],\n            [-1.9980, -2.0152, -1.9467,  ..., -0.4568, -0.3369, -0.1657],\n            [-1.9809, -1.9809, -1.9295,  ..., -0.8164, -0.5596, -0.3027],\n            ...,\n            [-1.9809, -1.9809, -1.9809,  ..., -1.0904, -1.1075, -1.1589],\n            [-1.9809, -1.9809, -1.9809,  ..., -1.0733, -1.1418, -1.2103],\n            [-1.9638, -1.9638, -1.9638,  ..., -1.0562, -1.1760, -1.2445]])\n\n\n\n**Change the shape**\n\nThe shape of two images are different, we can force the style image to be the same size as the content image. \n\n\n```python\ncontent.shape[-2:]\n```\n\n\n\n\n    torch.Size([400, 592])",
    "source": "2020-10-19-image-operations.md",
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  {
    "text": "```python\ncontent.shape[-2:]\n```\n\n\n\n\n    torch.Size([400, 592])\n\n\n\n\n```python\nstyle = load_image('/content/drive/My Drive/Style_Transfer/images/hockney.jpg', \n                   shape=content.shape[-2:]).to(device)\nprint(style.shape)\n```\n\n    torch.Size([1, 3, 400, 592])\n\n\n**Display the image**\n\n\n```python\n# helper function for un-normalizing an image \n# and converting it from a Tensor image to a NumPy image for display\ndef im_convert(tensor):\n    \"\"\" Display a tensor as an image. \"\"\"\n    \n    image = tensor.to(\"cpu\").clone().detach()\n    image = image.numpy().squeeze()\n    image = image.transpose(1,2,0)\n    image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\n    image = image.clip(0, 1)\n\n    return image\n```",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_7",
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  {
    "text": "return image\n```\n\n\n```python\n# display the images\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n# content and style ims side-by-side\nax1.imshow(im_convert(content))\nax2.imshow(im_convert(style))\n```\n\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/image_op/content_style.png\" width=\"50%\">\n  <div class=\"Figure 1: Content image(left) - Style image(right)\"> </div>\n</div>\n\n\n**See the explanation for each step of the function:**\n\n**Copy content image & detach**\n\nRef: https://stackoverflow.com/questions/63582590/why-do-we-call-detach-before-calling-numpy-on-a-pytorch-tensor",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_8",
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  {
    "text": "**Copy content image & detach**\n\nRef: https://stackoverflow.com/questions/63582590/why-do-we-call-detach-before-calling-numpy-on-a-pytorch-tensor\n\ntorch.tensors are designed to be used in the context of gradient descent optimization, and therefore they hold not only a tensor with numeric values, but (and more importantly) the computational graph leading to these values. This computational graph is then used (using the chain rule of derivatives) to compute the derivative of the loss function w.r.t each of the independent variables used to compute the loss.",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_9",
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  {
    "text": "As mentioned before, np.ndarray object does not have this extra \"computational graph\" layer and therefore, when converting a torch.tensor to np.ndarray you must explicitly remove the computational graph of the tensor using the detach() command.\n\n\n```python\nimage = content.to(\"cpu\").clone().detach()\nprint(image.shape)\nprint(image.type)\n```\n\n    torch.Size([1, 3, 400, 592])\n    <built-in method type of Tensor object at 0x7ff3dcba6e10>\n\n\n**Turn to numpy**\n\n\n```python\nimage = image.numpy()\nprint(image.shape)\nprint(image.dtype)\n```\n\n    (1, 3, 400, 592)\n    float32\n\n\n**Use squeeze**\n\nnumpy.squeeze() : Remove single-dimensional entries from the shape of an array.\n\n\n```python\nimage = image.squeeze()\nprint(image.shape)\n```\n\n    (3, 400, 592)\n\n\n**Transpose**",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_10",
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  {
    "text": "```python\nimage = image.squeeze()\nprint(image.shape)\n```\n\n    (3, 400, 592)\n\n\n**Transpose**\n\nTranspose the dimension such that: Height, Width, Channel\n- Original: 3, 400, 592\n- Transpose(1,2,0) --> 400, 592, 3 \n\n\n```python\nimage = image.transpose(1,2,0)\nimage.shape\n```\n\n\n\n\n    (400, 592, 3)\n\n\n\n**Normalize**\n\nRef: https://pytorch.org/docs/stable/torchvision/models.html\n\nAll pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. \n\nYou can use the following transform to normalize:",
    "source": "2020-10-19-image-operations.md",
    "type": "markdown",
    "chunk_id": "2020-10-19-image-operations.md_11",
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    "text": "You can use the following transform to normalize:\n\n        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                 std=[0.229, 0.224, 0.225])   \n\n\n```python\nimage = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\nimage.shape\n```\n\n\n\n\n    (400, 592, 3)\n\n\n\n**Clip the values**\n\nRef: https://numpy.org/doc/stable/reference/generated/numpy.clip.html\n\nGiven an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.\n\n\n\n```python\nimage = image.clip(0, 1)\nimage\n```",
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    "text": "Use matplotlib imshow to show the image\n\n\n```python\nplt.imshow(image)\n```\n\n\n\n\n    <matplotlib.image.AxesImage at 0x7ff3dae27cc0>\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/image_op/content.png\" width=\"50%\">\n  <div class=\"Content Image\"> </div>\n</div>\n\n\n\n**Summary:**\n\n- Load the image by using PIL\n- Turn it to Tensor\n- Change the shape\n- Visualize the image\n    - Why are we using constant terms when we normalize the image?\n    - What is squeeze?\n    - Transpose the 3 dimensional numpy array.\n    - How can we use clip?",
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  {
    "text": "---\ntitle: \"Motion Classifier with Edge Impulse\"\nid: \"edge-motion\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"arduino\", \"motion\", \"classification\", \"tensorflow\"]\nrelated: [\"arduinodeploy\"]\ndate: \"2021-01-01\"\ndescription: \"A motion classifier project with Edge Impulse & Arduino\"\n---\n\n\nHello! Happy new year everyone:) On the very first day of the year, I completed a motion classifier project with Edge Impulse & Arduino. \nI would like to tell you the steps of the project, some problems I've faced with and how to solve them.\n\nI would like to thank Coursera and Edge Impulse to give us a chance to learn embedded machine learning. \nFind more about the lecture: [Coursera](https://www.coursera.org/learn/introduction-to-embedded-machine-learning)",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_0",
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    "text": "### 1. Connection\n\nAfter creating a new project on Edge Impulse, I plugged in my Arduino board to my laptop, \nthen **update the firmware** ---> this is very important do not skip this step. \n\nThen I open the command prompt and write:\n\n    edge-impulse-daemon --clean\n\nI can connect my Arduino board with my new project in Edge Impulse. Go back to your project on the Edge Impulse website \nand check if the Arduino is connected. If you encountered with a problem, you can read my previous [post](https://pelinbalci.com/tinyml/2021/11/22/Arduino-Edge-Impulse.html)",
    "source": "2022-01-01-Motion-Classifier.md",
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  {
    "text": "### 2. Collect Data\n\nThis is the funny part. You need to choose an item in your home that you can collect motion data. \nI wish to have a remote-controlled car :( Anyway, I decided to use my hairdryer and taped the board to the hairdryer \nas you can see below.\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/hairdryer.jpeg\" width=\"40%\" height=\"60%\">\n  <div class=\"figcaption\">Setup </div>\n</div>\n\n--\n\nSelect Data- Acquisition, write the label name, and start sampling! There are 4 classes in my project, off, light, light_move, and heavy.\n\n    off: not working\n    light: working in mode 1\n    light_move: working in mode 1 and waving\n    heavy: working in mode 2\n\nHere is a short video of the collecting data process:\n\n--",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_2",
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  {
    "text": "Here is a short video of the collecting data process:\n\n-- \n\n<video width=\"320\" height=\"240\" controls>\n  <source src=\"/assets/image_assets/tinyml_motion_images/datacollect.mp4\" type=\"video/mp4\">\n</video>\n\n--\n\nI've collected 8 minutes of data (2 minutes for each class) and 2 minutes for test data.",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_3",
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  {
    "text": "### 3. Train & Test Model\n\nImpulse design is really easy thanks to Edge Impulse. First, you need to create impulse. \nYou will select processing block and learning block. The processing block will help you to generate \nfeatures, and the learning block is the classification model.\n\n-- \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/impulse_design.PNG\" width=\"80%\">\n  <div class=\"figcaption\">Impulse Design </div>\n</div>\n\n--\n\nYou can check my previous post about [Fourier Transform](https://pelinbalci.com/2021/12/19/Fast-Fourier-Tranform.html), \nIt is used for generating features, fortunately, the Edge Impulse handles the Fouirer Transform for us.",
    "source": "2022-01-01-Motion-Classifier.md",
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  {
    "text": "After feature generation, I designed a neural network with 2 hidden layers and dropout layer with probability of 0.2",
    "source": "2022-01-01-Motion-Classifier.md",
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  },
  {
    "text": "import tensorflow as tf\n    from tensorflow.keras.models import Sequential\n    from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed\n    from tensorflow.keras.optimizers import Adam\n    \n    # model architecture\n    model = Sequential()\n    model.add(Dense(64, activation='relu',\n        activity_regularizer=tf.keras.regularizers.l1(0.00001)))\n    model.add(Dense(32, activation='relu',\n        activity_regularizer=tf.keras.regularizers.l1(0.00001)))\n    model.add(Dropout(0.2))\n    model.add(Dense(classes, activation='softmax', name='y_pred'))\n    \n    # this controls the learning rate\n    opt = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_6",
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  {
    "text": "# this controls the learning rate\n    opt = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)\n    # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself\n    BATCH_SIZE = 32\n    train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)\n    validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)\n    callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count))\n    \n    # train the neural network\n    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n    model.fit(train_dataset, epochs=30, validation_data=validation_dataset, verbose=2, callbacks=callbacks)",
    "source": "2022-01-01-Motion-Classifier.md",
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  {
    "text": "Here is the validation score:\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/valid_acc.PNG\" width=\"70%\">\n  <div class=\"figcaption\">Validation Accuracy :) </div>\n</div>\n\n--\n\nThen I choose model testing. The test score is 71.45%. I am happy with this result.\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/test_acc.PNG\" width=\"70%\">\n  <div class=\"figcaption\">Test Accuracy :) </div>\n</div>",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_8",
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  {
    "text": "### 4. Deployment\nYou can read my previous [post](https://pelinbalci.com/tinyml/2021/11/28/Deploy-To-Arduino.html) about deployment. Click Deployment, select Arduino, and the quantized version of the project (Please read more about Tensorflow Lite). Click build --> this will give you a zip file.\n\nNow please close the terminal if it still opens. (I got \"port is busy\" error during uploading before I close the terminal)\n\nOpen Arduino IDE.\n\n- Sketch --> Include library --> Add Zip Library\n- File --> Examples --> motion_classifier_inferencing --> nano_ble33_sense_accelerometer --> upload --> this will take around 10 minutes.",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_9",
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  {
    "text": "Then you can open Serial Monitor for live classification. Here is a short video for live classification. \nThere are some errors between the targets light and heavy but that's ok :)\n\n<video width=\"320\" height=\"240\" controls>\n  <source src=\"/assets/image_assets/tinyml_motion_images/live_class.mp4\" type=\"video/mp4\">\n</video>\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/live_classification.PNG\" width=\"80%\">\n  <div class=\"figcaption\">Live Classification </div>\n</div>",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_10",
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  {
    "text": "### 5. Improvement\n\nI tried to increase the test accuracy for all classes. First I changed the model architecture:",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_11",
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  },
  {
    "text": "import tensorflow as tf\n    from tensorflow.keras.models import Sequential\n    from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed\n    from tensorflow.keras.optimizers import Adam\n    \n    # model architecture\n    model = Sequential()\n    model.add(Dense(128, activation='relu',\n        activity_regularizer=tf.keras.regularizers.l1(0.00001)))\n    model.add(Dense(64, activation='relu',\n        activity_regularizer=tf.keras.regularizers.l1(0.00001)))\n    model.add(Dropout(0.25))\n    model.add(Dense(classes, activation='softmax', name='y_pred'))\n    \n    # this controls the learning rate\n    opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999)",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_12",
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  {
    "text": "# this controls the learning rate\n    opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999)\n    # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself\n    BATCH_SIZE = 32\n    train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)\n    validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)\n    callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count))\n    \n    # train the neural network\n    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n    model.fit(train_dataset, epochs=30, validation_data=validation_dataset, verbose=2, callbacks=callbacks)",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_13",
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    "text": "Then, I've changed the parameters in Spectral features.\n\nDefault filter value is low. Let's try \"heavy\" filter. The features separates the classes well but the test accuracy is worse than the low filter:\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/feature_heavy.png\" width=\"70%\">\n  <div class=\"figcaption\">Heavy Filter </div>\n</div>\n\n--\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/test_heavy.png\" width=\"70%\">\n  <div class=\"figcaption\">Heavy Filter Test Accuracy </div>\n</div>\n\n--\n\nFilter None gives very good result for test data :)",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
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  {
    "text": "--\n\nFilter None gives very good result for test data :) \n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/feature_none.png\" width=\"70%\">\n  <div class=\"figcaption\">None Filter </div>\n</div>\n\n<div class=\"fig figcenter fighighlight\">\n  <img src=\"/assets/image_assets/tinyml_motion_images/test_none.png\" width=\"70%\">\n  <div class=\"figcaption\">None Filter Test Accuracy </div>\n</div>",
    "source": "2022-01-01-Motion-Classifier.md",
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  {
    "text": "### Key Words\nTiny ML, Arduino, Edge Impulse, Motion, Classification",
    "source": "2022-01-01-Motion-Classifier.md",
    "type": "markdown",
    "chunk_id": "2022-01-01-Motion-Classifier.md_16",
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  {
    "text": "---\ntitle: \"Langchain Tools\"\nid: \"langchain_tools\"\ncategory: \"genai\"\ntags: [\"agents\", \"llm\"]\nrelated: [\"agentic-architecture\"]\ndate: \"2025-11-25\"\ndescription: \"How LangChain calls the custom functions and how to leverage Tavily Search for internet searching.\"\n---\n\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/getting-started-with-langchain-tools-3beec9e1fb95) \n\nAre you curious about AI agents and ready to build them from scratch? This post will be a great start for you. I will \nshow you how LangChain calls the custom functions and how to leverage Tavily Search for internet searching. In future \nposts, we will dive into how these constructs fit into the agentic architecture. Stay tuned to learn more about AI agents!✨🎉",
    "source": "2025-01-18-LangChain_Tools.md",
    "type": "markdown",
    "chunk_id": "2025-01-18-LangChain_Tools.md_0",
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  {
    "text": "---\ntitle: \"Quantization 1\"\nid: \"quantization\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"quantization\", \"medium\"]\nrelated: [\"finetuning\", \"dl\"]\ndate: \"2024-05-05\"\ndescription: \"Unlocking the Power of Quantization: From Float32 to Int8\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/quantization-1-d05e5a61e0af) \n\nWhy do we need quantization?\n* Shrink models to a small size.\n* DL architectures are bigger and bigger.\n* A model can have 70 billion parameters.\n* NVIDIA T4 GPUs have 16 GB RAM.\n* Running models are still a challenge.\n* The aim is to get a smaller model.",
    "source": "2024-05-05-Quantization_1.md",
    "type": "markdown",
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  {
    "text": "---\ntitle: \"Anomaly Detection\"\nid: \"anomaly_numpy\"\ncategory: \"machine-learning\"\ntags: [\"anomaly\", \"numpy\"]\nrelated: [\"ml\"]\ndate: \"2022-11-05\"\ndescription: \"Notebook for anomaly detection with NumPy\"\n---\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/2022-11-05-Anomaly_Detection_with_Numpy.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>",
    "source": "2022-11-05-Anomaly_Detection_with_Numpy.md",
    "type": "markdown",
    "chunk_id": "2022-11-05-Anomaly_Detection_with_Numpy.md_0",
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  {
    "text": "---\ntitle: \"K-means with Numpy\"\nid: \"kmeans\"\ncategory: \"machine-learning\"\ntags: [\"machine learning\", \"unsupervised learning\", \"numpy\", \"medium\"]\nrelated: [\"ml\"]\ndate: \"2023-03-07\"\ndescription: \"Explanation of K-means algorithm with Numpy\"\n---\n\nIn this post, I will explain the K-means algorithm via Numpy. Continue reading by clicking [here](https://medium.com/@balci.pelin/k-means-with-numpy-3c207398c4d4)",
    "source": "2023-03-07-K_means_with_numpy.md",
    "type": "markdown",
    "chunk_id": "2023-03-07-K_means_with_numpy.md_0",
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  {
    "text": "---\ntitle: \"Review of Arduino TinyML Kit Sensors\"\nid: \"arduinosensor\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"sensor\", \"arduino\", \"medium\"]\nrelated: [\"dl\"]\ndate: \"2025-09-03\"\ndescription: \"Getting Started with Tiny Machine Learning\"\n---\n\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/review-of-arduino-tinyml-kit-sensors-b982f00518c7)\n\nBefore jumping into machine learning, it’s fun to simply play with the kit — blink an LED, read motion data, or snap a picture. In this post, we’ll walk through some quick tests (Blink → IMU → Camera) that help you explore the hardware and get comfortable before machine learning applications.",
    "source": "2025-09-03-ArduinoTinyMLKitSensors.md",
    "type": "markdown",
    "chunk_id": "2025-09-03-ArduinoTinyMLKitSensors.md_0",
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  {
    "text": "---\ntitle: \"Fast Fourier Transform\"\nid: \"fft\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"fft\", \"audio\"]\nrelated: [\"edge-audio\"]\ndate: \"2021-01-01\"\ndescription: \"A motion classifier project with Edge Impulse & Arduino\"\n---\n\n\n<div>\n<iframe \n    src=\"https://www.pelinbalci.com/assets/components/2021-12-19-Fast Fourier Tranform.html\" \n    width=\"100%\" \n    height=\"600\" \n    style=\"border:none;\"\n></iframe>\n</div>",
    "source": "2021-12-19-Fast_Fourier_Transform.md",
    "type": "markdown",
    "chunk_id": "2021-12-19-Fast_Fourier_Transform.md_0",
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    "text": "---\ntitle: \"LLM Introduction\"\nid: \"llm\"\ncategory: \"genai\"\ntags: [\"llm\", \"medium\"]\nrelated: []\ndate: \"2023-07-17\"\ndescription: \"LLM Introduction\"\n---\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/llm-introduction-7ededa51b78b)\n\nIf you think that, now that ChatGPT handles everything and you are late to learn it, this article is for you:)",
    "source": "2023-07-17-LLM_Intro.md",
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  {
    "text": "---\ntitle: \"Introduction to Deep Learning\"\nid: \"dl\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"neural networks\", \"PyTorch\", \"CNN\", \"RNN\", \"attention\", \"ai-generated\"]\nrelated: [\"transformers\"]\ndate: \"2025-11-24\"\ndescription: \"Comprehensive introduction to deep learning concepts including neural networks, CNNs, RNNs, attention mechanisms, and backpropagation with PyTorch examples\"\n---\n\n# Introduction to Deep Learning\n\n**This article is generated by Claude Opus 4.5**\n\n## Overview\n\nDeep Learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence \"deep\") to learn complex patterns from data. It has revolutionized fields like computer vision, natural language processing, and speech recognition.",
    "source": "dl.md",
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  {
    "text": "## What Makes Deep Learning Different?\n\nWhile traditional machine learning requires manual feature engineering, deep learning automatically learns hierarchical representations from raw data. Each layer learns increasingly abstract features.\n\n### Key Concepts\n\n- **Neural Network**: A computational model inspired by biological neurons\n- **Layers**: Building blocks that transform input data\n- **Weights & Biases**: Learnable parameters that the network optimizes\n- **Activation Functions**: Non-linear functions that enable learning complex patterns\n- **Loss Function**: Measures how well the model's predictions match the targets\n- **Backpropagation**: Algorithm for computing gradients to update weights",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_1",
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  {
    "text": "## Getting Started with PyTorch\n\nPyTorch is a popular deep learning framework known for its flexibility and Pythonic design.\n\n### Installation\n\n```bash\npip install torch torchvision\n```\n\n### Basic Tensor Operations\n\n```python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Create tensors\nx = torch.tensor([1.0, 2.0, 3.0])\ny = torch.tensor([4.0, 5.0, 6.0])\n\n# Basic operations\nz = x + y           # Element-wise addition\ndot = torch.dot(x, y)  # Dot product\n\n# Create a matrix\nmatrix = torch.randn(3, 4)  # Random 3x4 matrix\nprint(f\"Matrix shape: {matrix.shape}\")\n\n# GPU support (if available)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ntensor_gpu = matrix.to(device)\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_2",
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  {
    "text": "## Neural Network Fundamentals\n\n### The Perceptron (Single Neuron)\n\n```python\nimport torch\nimport torch.nn as nn\n\nclass Perceptron(nn.Module):\n    def __init__(self, input_size):\n        super().__init__()\n        self.linear = nn.Linear(input_size, 1)\n        self.activation = nn.Sigmoid()\n    \n    def forward(self, x):\n        return self.activation(self.linear(x))\n\n# Create and use perceptron\nperceptron = Perceptron(input_size=3)\ninput_data = torch.tensor([1.0, 2.0, 3.0])\noutput = perceptron(input_data)\nprint(f\"Output: {output.item():.4f}\")\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_3",
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  {
    "text": "### Multi-Layer Perceptron (MLP)\n\n```python\nclass MLP(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super().__init__()\n        self.network = nn.Sequential(\n            nn.Linear(input_size, hidden_size),\n            nn.ReLU(),\n            nn.Linear(hidden_size, hidden_size),\n            nn.ReLU(),\n            nn.Linear(hidden_size, output_size)\n        )\n    \n    def forward(self, x):\n        return self.network(x)\n\n# Example: Classification with 10 input features, 64 hidden units, 3 classes\nmodel = MLP(input_size=10, hidden_size=64, output_size=3)\nsample_input = torch.randn(1, 10)  # Batch size 1\noutput = model(sample_input)\nprint(f\"Output shape: {output.shape}\")  # [1, 3]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_4",
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  {
    "text": "## Backpropagation\n\nBackpropagation is the algorithm used to compute gradients of the loss with respect to each weight. It applies the chain rule of calculus to efficiently propagate errors backward through the network.\n\n### How Backpropagation Works\n\n1. **Forward Pass**: Compute predictions by passing input through the network\n2. **Compute Loss**: Calculate the difference between predictions and targets\n3. **Backward Pass**: Compute gradients using the chain rule\n4. **Update Weights**: Adjust weights in the direction that reduces loss",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_5",
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    "text": "### Manual Backpropagation Example\n\n```python\nimport torch\n\n# Simple network: y = w2 * relu(w1 * x + b1) + b2\nx = torch.tensor([1.0, 2.0, 3.0], requires_grad=False)\ntarget = torch.tensor([1.0])\n\n# Initialize weights with gradient tracking\nw1 = torch.randn(3, 4, requires_grad=True)\nb1 = torch.zeros(4, requires_grad=True)\nw2 = torch.randn(4, 1, requires_grad=True)\nb2 = torch.zeros(1, requires_grad=True)\n\n# Forward pass\nhidden = torch.relu(x @ w1 + b1)\noutput = hidden @ w2 + b2\n\n# Compute loss (MSE)\nloss = ((output - target) ** 2).mean()\nprint(f\"Loss: {loss.item():.4f}\")\n\n# Backward pass - computes all gradients\nloss.backward()\n\n# Gradients are now available\nprint(f\"w1 gradient shape: {w1.grad.shape}\")\nprint(f\"w2 gradient shape: {w2.grad.shape}\")\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_6",
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  {
    "text": "### Training Loop with PyTorch\n\n```python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Create model, loss function, and optimizer\nmodel = MLP(input_size=10, hidden_size=64, output_size=3)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# Training loop\ndef train_step(model, X_batch, y_batch):\n    model.train()\n    \n    # Forward pass\n    outputs = model(X_batch)\n    loss = criterion(outputs, y_batch)\n    \n    # Backward pass\n    optimizer.zero_grad()  # Clear previous gradients\n    loss.backward()        # Compute gradients\n    optimizer.step()       # Update weights\n    \n    return loss.item()\n\n# Example training\nX_train = torch.randn(100, 10)  # 100 samples\ny_train = torch.randint(0, 3, (100,))  # 3 classes",
    "source": "dl.md",
    "type": "markdown",
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  },
  {
    "text": "# Example training\nX_train = torch.randn(100, 10)  # 100 samples\ny_train = torch.randint(0, 3, (100,))  # 3 classes\n\nfor epoch in range(10):\n    loss = train_step(model, X_train, y_train)\n    print(f\"Epoch {epoch+1}, Loss: {loss:.4f}\")\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_8",
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  {
    "text": "## Convolutional Neural Networks (CNNs)\n\nCNNs are designed for processing grid-like data such as images. They use convolutional layers to automatically learn spatial hierarchies of features.\n\n### Key Components\n\n- **Convolutional Layer**: Applies filters to detect local patterns\n- **Pooling Layer**: Reduces spatial dimensions while retaining important features\n- **Stride**: Step size when sliding the filter\n- **Padding**: Adding zeros around input to control output size",
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  {
    "text": "### CNN Architecture in PyTorch\n\n```python\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F",
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  },
  {
    "text": "class CNN(nn.Module):\n    def __init__(self, num_classes=10):\n        super().__init__()\n        \n        # Convolutional layers\n        self.conv1 = nn.Conv2d(\n            in_channels=1,    # Grayscale image\n            out_channels=32,  # 32 filters\n            kernel_size=3,    # 3x3 filter\n            padding=1         # Same padding\n        )\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        \n        # Pooling layer\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n        \n        # Batch normalization\n        self.bn1 = nn.BatchNorm2d(32)\n        self.bn2 = nn.BatchNorm2d(64)\n        self.bn3 = nn.BatchNorm2d(128)\n        \n        # Fully connected layers",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_11",
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  {
    "text": "self.bn2 = nn.BatchNorm2d(64)\n        self.bn3 = nn.BatchNorm2d(128)\n        \n        # Fully connected layers\n        self.fc1 = nn.Linear(128 * 3 * 3, 256)  # After 3 pooling ops: 28->14->7->3\n        self.fc2 = nn.Linear(256, num_classes)\n        \n        # Dropout for regularization\n        self.dropout = nn.Dropout(0.5)\n    \n    def forward(self, x):\n        # Conv block 1: [B, 1, 28, 28] -> [B, 32, 14, 14]\n        x = self.pool(F.relu(self.bn1(self.conv1(x))))\n        \n        # Conv block 2: [B, 32, 14, 14] -> [B, 64, 7, 7]\n        x = self.pool(F.relu(self.bn2(self.conv2(x))))\n        \n        # Conv block 3: [B, 64, 7, 7] -> [B, 128, 3, 3]\n        x = self.pool(F.relu(self.bn3(self.conv3(x))))\n        \n        # Flatten: [B, 128, 3, 3] -> [B, 128*3*3]",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_12",
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  },
  {
    "text": "x = self.pool(F.relu(self.bn3(self.conv3(x))))\n        \n        # Flatten: [B, 128, 3, 3] -> [B, 128*3*3]\n        x = x.view(x.size(0), -1)\n        \n        # Fully connected layers\n        x = self.dropout(F.relu(self.fc1(x)))\n        x = self.fc2(x)\n        \n        return x",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_13",
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  {
    "text": "# Create model and test with random image\ncnn = CNN(num_classes=10)\nsample_image = torch.randn(1, 1, 28, 28)  # Batch=1, Channels=1, H=28, W=28\noutput = cnn(sample_image)\nprint(f\"CNN output shape: {output.shape}\")  # [1, 10]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_14",
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  {
    "text": "### Understanding Convolutions\n\n```python\n# Visualize what a convolution does\nimport torch\nimport torch.nn as nn\n\n# Create a simple edge detection filter\nedge_filter = torch.tensor([\n    [-1, -1, -1],\n    [-1,  8, -1],\n    [-1, -1, -1]\n], dtype=torch.float32).view(1, 1, 3, 3)\n\n# Apply to a sample image\nconv = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False)\nconv.weight.data = edge_filter\n\nsample = torch.randn(1, 1, 8, 8)\nfiltered = conv(sample)\nprint(f\"Input shape: {sample.shape}, Output shape: {filtered.shape}\")\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_15",
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  },
  {
    "text": "## Recurrent Neural Networks (RNNs)\n\nRNNs are designed for sequential data where the order matters (text, time series, audio). They maintain a hidden state that captures information from previous time steps.",
    "source": "dl.md",
    "type": "markdown",
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  },
  {
    "text": "### Basic RNN\n\n```python\nimport torch\nimport torch.nn as nn",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_17",
    "embedding": [
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  {
    "text": "class SimpleRNN(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super().__init__()\n        self.hidden_size = hidden_size\n        \n        # RNN layer\n        self.rnn = nn.RNN(\n            input_size=input_size,\n            hidden_size=hidden_size,\n            num_layers=1,\n            batch_first=True  # Input shape: [batch, seq_len, features]\n        )\n        \n        # Output layer\n        self.fc = nn.Linear(hidden_size, output_size)\n    \n    def forward(self, x, hidden=None):\n        # x shape: [batch, seq_len, input_size]\n        \n        # Initialize hidden state if not provided\n        if hidden is None:\n            hidden = torch.zeros(1, x.size(0), self.hidden_size)\n        \n        # RNN forward pass",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_18",
    "embedding": [
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  },
  {
    "text": "if hidden is None:\n            hidden = torch.zeros(1, x.size(0), self.hidden_size)\n        \n        # RNN forward pass\n        output, hidden = self.rnn(x, hidden)\n        # output shape: [batch, seq_len, hidden_size]\n        # hidden shape: [num_layers, batch, hidden_size]\n        \n        # Take the last output for classification\n        last_output = output[:, -1, :]  # [batch, hidden_size]\n        prediction = self.fc(last_output)\n        \n        return prediction, hidden",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_19",
    "embedding": [
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  },
  {
    "text": "# Example: Sequence classification\nrnn = SimpleRNN(input_size=10, hidden_size=64, output_size=5)\nsequence = torch.randn(32, 20, 10)  # Batch=32, SeqLen=20, Features=10\noutput, _ = rnn(sequence)\nprint(f\"RNN output shape: {output.shape}\")  # [32, 5]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_20",
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  },
  {
    "text": "### LSTM (Long Short-Term Memory)\n\nLSTMs solve the vanishing gradient problem in vanilla RNNs using gates that control information flow.",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_21",
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  },
  {
    "text": "```python\nclass LSTMClassifier(nn.Module):\n    def __init__(self, vocab_size, embed_dim, hidden_size, num_classes):\n        super().__init__()\n        \n        # Embedding layer for text\n        self.embedding = nn.Embedding(vocab_size, embed_dim)\n        \n        # LSTM layer\n        self.lstm = nn.LSTM(\n            input_size=embed_dim,\n            hidden_size=hidden_size,\n            num_layers=2,\n            batch_first=True,\n            dropout=0.3,\n            bidirectional=True  # Process sequence both directions\n        )\n        \n        # Output layer (bidirectional doubles hidden size)\n        self.fc = nn.Linear(hidden_size * 2, num_classes)\n    \n    def forward(self, x):\n        # x shape: [batch, seq_len] - token indices\n        \n        # Embed tokens",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_22",
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  {
    "text": "def forward(self, x):\n        # x shape: [batch, seq_len] - token indices\n        \n        # Embed tokens\n        embedded = self.embedding(x)  # [batch, seq_len, embed_dim]\n        \n        # LSTM forward pass\n        lstm_out, (hidden, cell) = self.lstm(embedded)\n        # lstm_out: [batch, seq_len, hidden_size*2]\n        # hidden: [num_layers*2, batch, hidden_size]\n        \n        # Concatenate final hidden states from both directions\n        hidden_cat = torch.cat([hidden[-2], hidden[-1]], dim=1)\n        \n        # Classification\n        output = self.fc(hidden_cat)\n        return output",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_23",
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  },
  {
    "text": "# Example: Text classification\nlstm_model = LSTMClassifier(vocab_size=10000, embed_dim=128, hidden_size=256, num_classes=3)\ntext_input = torch.randint(0, 10000, (16, 50))  # Batch=16, SeqLen=50\noutput = lstm_model(text_input)\nprint(f\"LSTM output shape: {output.shape}\")  # [16, 3]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_24",
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  },
  {
    "text": "### GRU (Gated Recurrent Unit)\n\nGRUs are a simpler alternative to LSTMs with fewer parameters.\n\n```python\nclass GRUModel(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size, num_layers=2):\n        super().__init__()\n        \n        self.gru = nn.GRU(\n            input_size=input_size,\n            hidden_size=hidden_size,\n            num_layers=num_layers,\n            batch_first=True,\n            dropout=0.2\n        )\n        \n        self.fc = nn.Linear(hidden_size, output_size)\n    \n    def forward(self, x):\n        output, hidden = self.gru(x)\n        # Use last time step output\n        return self.fc(output[:, -1, :])",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_25",
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  },
  {
    "text": "# Time series prediction\ngru = GRUModel(input_size=1, hidden_size=64, output_size=1)\ntime_series = torch.randn(32, 100, 1)  # 100 time steps\nprediction = gru(time_series)\nprint(f\"GRU prediction shape: {prediction.shape}\")  # [32, 1]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_26",
    "embedding": [
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  },
  {
    "text": "## Attention Mechanisms\n\nAttention allows models to focus on relevant parts of the input when making predictions. It's the foundation of modern architectures like Transformers.",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_27",
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  },
  {
    "text": "### Basic Attention\n\n```python\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_28",
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  },
  {
    "text": "class Attention(nn.Module):\n    \"\"\"Simple additive attention mechanism.\"\"\"\n    \n    def __init__(self, hidden_size):\n        super().__init__()\n        self.attention = nn.Linear(hidden_size, 1)\n    \n    def forward(self, encoder_outputs):\n        # encoder_outputs: [batch, seq_len, hidden_size]\n        \n        # Compute attention scores\n        scores = self.attention(encoder_outputs)  # [batch, seq_len, 1]\n        scores = scores.squeeze(-1)  # [batch, seq_len]\n        \n        # Softmax to get attention weights\n        weights = F.softmax(scores, dim=1)  # [batch, seq_len]\n        \n        # Weighted sum of encoder outputs\n        context = torch.bmm(\n            weights.unsqueeze(1),  # [batch, 1, seq_len]\n            encoder_outputs         # [batch, seq_len, hidden_size]",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_29",
    "embedding": [
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  {
    "text": "weights.unsqueeze(1),  # [batch, 1, seq_len]\n            encoder_outputs         # [batch, seq_len, hidden_size]\n        ).squeeze(1)  # [batch, hidden_size]\n        \n        return context, weights\n```",
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  },
  {
    "text": "### Scaled Dot-Product Attention\n\nThis is the attention mechanism used in Transformers.",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_31",
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  },
  {
    "text": "```python\nclass ScaledDotProductAttention(nn.Module):\n    def __init__(self, d_k):\n        super().__init__()\n        self.scale = d_k ** 0.5\n    \n    def forward(self, query, key, value, mask=None):\n        \"\"\"\n        Args:\n            query: [batch, seq_len, d_k]\n            key: [batch, seq_len, d_k]\n            value: [batch, seq_len, d_v]\n            mask: Optional mask for padding or causal attention\n        \"\"\"\n        # Compute attention scores\n        scores = torch.bmm(query, key.transpose(1, 2)) / self.scale\n        # scores: [batch, seq_len, seq_len]\n        \n        # Apply mask if provided\n        if mask is not None:\n            scores = scores.masked_fill(mask == 0, float('-inf'))\n        \n        # Softmax to get attention weights",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_32",
    "embedding": [
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  {
    "text": "if mask is not None:\n            scores = scores.masked_fill(mask == 0, float('-inf'))\n        \n        # Softmax to get attention weights\n        attention_weights = F.softmax(scores, dim=-1)\n        \n        # Weighted sum of values\n        output = torch.bmm(attention_weights, value)\n        \n        return output, attention_weights",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_33",
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  },
  {
    "text": "# Example usage\nd_k = 64\nattention = ScaledDotProductAttention(d_k)\n\nbatch_size, seq_len = 8, 20\nQ = torch.randn(batch_size, seq_len, d_k)\nK = torch.randn(batch_size, seq_len, d_k)\nV = torch.randn(batch_size, seq_len, d_k)\n\noutput, weights = attention(Q, K, V)\nprint(f\"Attention output shape: {output.shape}\")  # [8, 20, 64]\nprint(f\"Attention weights shape: {weights.shape}\")  # [8, 20, 20]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_34",
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  },
  {
    "text": "### Multi-Head Attention\n\nMulti-head attention allows the model to attend to different representation subspaces.",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_35",
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  },
  {
    "text": "```python\nclass MultiHeadAttention(nn.Module):\n    def __init__(self, d_model, num_heads):\n        super().__init__()\n        assert d_model % num_heads == 0\n        \n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.d_k = d_model // num_heads\n        \n        # Linear projections for Q, K, V\n        self.W_q = nn.Linear(d_model, d_model)\n        self.W_k = nn.Linear(d_model, d_model)\n        self.W_v = nn.Linear(d_model, d_model)\n        \n        # Output projection\n        self.W_o = nn.Linear(d_model, d_model)\n        \n        self.attention = ScaledDotProductAttention(self.d_k)\n    \n    def forward(self, query, key, value, mask=None):\n        batch_size = query.size(0)\n        \n        # Linear projections and reshape for multi-head",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_36",
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  },
  {
    "text": "batch_size = query.size(0)\n        \n        # Linear projections and reshape for multi-head\n        Q = self.W_q(query).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)\n        K = self.W_k(key).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)\n        V = self.W_v(value).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)\n        # Q, K, V shape: [batch, num_heads, seq_len, d_k]\n        \n        # Reshape for batch matrix multiplication\n        Q = Q.reshape(batch_size * self.num_heads, -1, self.d_k)\n        K = K.reshape(batch_size * self.num_heads, -1, self.d_k)\n        V = V.reshape(batch_size * self.num_heads, -1, self.d_k)\n        \n        # Apply attention\n        attended, weights = self.attention(Q, K, V, mask)",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_37",
    "embedding": [
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  },
  {
    "text": "# Apply attention\n        attended, weights = self.attention(Q, K, V, mask)\n        \n        # Reshape back\n        attended = attended.view(batch_size, self.num_heads, -1, self.d_k)\n        attended = attended.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)\n        \n        # Final linear projection\n        output = self.W_o(attended)\n        \n        return output",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_38",
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  },
  {
    "text": "# Example\nmha = MultiHeadAttention(d_model=512, num_heads=8)\nx = torch.randn(4, 30, 512)  # Batch=4, SeqLen=30, Dim=512\noutput = mha(x, x, x)  # Self-attention\nprint(f\"Multi-head attention output: {output.shape}\")  # [4, 30, 512]\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_39",
    "embedding": [
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  },
  {
    "text": "### Self-Attention with RNN\n\nCombining attention with RNNs for sequence classification.",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_40",
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  },
  {
    "text": "```python\nclass AttentionRNN(nn.Module):\n    def __init__(self, vocab_size, embed_dim, hidden_size, num_classes):\n        super().__init__()\n        \n        self.embedding = nn.Embedding(vocab_size, embed_dim)\n        \n        self.lstm = nn.LSTM(\n            embed_dim, hidden_size,\n            batch_first=True, bidirectional=True\n        )\n        \n        self.attention = Attention(hidden_size * 2)\n        self.fc = nn.Linear(hidden_size * 2, num_classes)\n    \n    def forward(self, x):\n        # Embed\n        embedded = self.embedding(x)\n        \n        # LSTM encoding\n        lstm_out, _ = self.lstm(embedded)\n        \n        # Apply attention to get context vector\n        context, attention_weights = self.attention(lstm_out)\n        \n        # Classify",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_41",
    "embedding": [
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  },
  {
    "text": "# Apply attention to get context vector\n        context, attention_weights = self.attention(lstm_out)\n        \n        # Classify\n        output = self.fc(context)\n        \n        return output, attention_weights",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_42",
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  },
  {
    "text": "# Example\nmodel = AttentionRNN(vocab_size=5000, embed_dim=128, hidden_size=128, num_classes=2)\ntext = torch.randint(0, 5000, (8, 50))\noutput, attn_weights = model(text)\nprint(f\"Output: {output.shape}, Attention: {attn_weights.shape}\")\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_43",
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  },
  {
    "text": "## Complete Training Example\n\nHere's a full example training a CNN on image classification:\n\n```python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_44",
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  },
  {
    "text": "```python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset\n\n# Model\nclass ImageClassifier(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.features = nn.Sequential(\n            nn.Conv2d(3, 32, 3, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(2),\n            nn.Conv2d(32, 64, 3, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(2),\n            nn.Conv2d(64, 128, 3, padding=1),\n            nn.ReLU(),\n            nn.AdaptiveAvgPool2d(1)\n        )\n        self.classifier = nn.Linear(128, 10)\n    \n    def forward(self, x):\n        x = self.features(x)\n        x = x.view(x.size(0), -1)\n        return self.classifier(x)",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_45",
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  },
  {
    "text": "# Training function\ndef train(model, train_loader, epochs=10, device='cpu'):\n    model = model.to(device)\n    criterion = nn.CrossEntropyLoss()\n    optimizer = optim.Adam(model.parameters(), lr=0.001)\n    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)\n    \n    for epoch in range(epochs):\n        model.train()\n        total_loss = 0\n        correct = 0\n        total = 0\n        \n        for batch_x, batch_y in train_loader:\n            batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n            \n            optimizer.zero_grad()\n            outputs = model(batch_x)\n            loss = criterion(outputs, batch_y)\n            loss.backward()\n            optimizer.step()\n            \n            total_loss += loss.item()",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_46",
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  {
    "text": "loss.backward()\n            optimizer.step()\n            \n            total_loss += loss.item()\n            _, predicted = outputs.max(1)\n            correct += predicted.eq(batch_y).sum().item()\n            total += batch_y.size(0)\n        \n        scheduler.step()\n        \n        accuracy = 100. * correct / total\n        print(f\"Epoch {epoch+1}/{epochs} - Loss: {total_loss:.4f} - Accuracy: {accuracy:.2f}%\")",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_47",
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  },
  {
    "text": "# Create sample data and train\nX = torch.randn(1000, 3, 32, 32)  # 1000 RGB images 32x32\ny = torch.randint(0, 10, (1000,))  # 10 classes\n\ndataset = TensorDataset(X, y)\nloader = DataLoader(dataset, batch_size=32, shuffle=True)\n\nmodel = ImageClassifier()\ntrain(model, loader, epochs=5)\n```",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_48",
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  {
    "text": "## Activation Functions\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nx = torch.linspace(-5, 5, 100)\n\n# Common activation functions\nrelu = F.relu(x)           # max(0, x)\nsigmoid = torch.sigmoid(x)  # 1 / (1 + e^-x)\ntanh = torch.tanh(x)        # (e^x - e^-x) / (e^x + e^-x)\nleaky_relu = F.leaky_relu(x, 0.01)  # x if x > 0 else 0.01*x\ngelu = F.gelu(x)            # Used in Transformers\n\n# In a model\nmodel = nn.Sequential(\n    nn.Linear(10, 64),\n    nn.GELU(),  # or nn.ReLU(), nn.LeakyReLU(), etc.\n    nn.Linear(64, 10)\n)\n```",
    "source": "dl.md",
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  {
    "text": "## Loss Functions\n\n```python\n# Classification\nce_loss = nn.CrossEntropyLoss()      # Multi-class\nbce_loss = nn.BCEWithLogitsLoss()    # Binary\n\n# Regression\nmse_loss = nn.MSELoss()              # Mean Squared Error\nmae_loss = nn.L1Loss()               # Mean Absolute Error\nhuber_loss = nn.SmoothL1Loss()       # Robust to outliers\n\n# Sequence models\nctc_loss = nn.CTCLoss()              # Speech recognition\n\n# Example usage\npredictions = torch.randn(32, 10)    # 32 samples, 10 classes\ntargets = torch.randint(0, 10, (32,))\nloss = ce_loss(predictions, targets)\n```",
    "source": "dl.md",
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  {
    "text": "## Key Takeaways\n\n- Deep learning uses multiple layers to learn hierarchical representations\n- Backpropagation efficiently computes gradients using the chain rule\n- CNNs excel at spatial data (images) using convolutional filters\n- RNNs/LSTMs/GRUs process sequential data with memory\n- Attention mechanisms allow models to focus on relevant input parts\n- PyTorch provides a flexible framework for building and training models\n- Always use GPU acceleration when available for faster training",
    "source": "dl.md",
    "type": "markdown",
    "chunk_id": "dl.md_51",
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  {
    "text": "## Resources for Learning\n\n### Online Courses\n- [Deep Learning Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning)\n- [Fast.ai Practical Deep Learning](https://course.fast.ai/)\n- [Stanford CS231n: CNNs for Visual Recognition](http://cs231n.stanford.edu/)\n- [Stanford CS224n: NLP with Deep Learning](http://web.stanford.edu/class/cs224n/)\n\n### Books\n- \"Deep Learning\" by Goodfellow, Bengio, and Courville\n- \"Dive into Deep Learning\" (d2l.ai) - Free online book\n- \"Neural Networks and Deep Learning\" by Michael Nielsen\n\n### Documentation\n- [PyTorch Documentation](https://pytorch.org/docs/)\n- [PyTorch Tutorials](https://pytorch.org/tutorials/)",
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  {
    "text": "## Related Topics\n\n- [Machine Learning Fundamentals](ml.html) - ML basics before diving into DL\n- [Python Basics](python.html) - Programming foundation\n- [Transformers](transformers.html) - Modern attention-based architectures\n\n## Tools and Libraries\n\n- **PyTorch**: Flexible deep learning framework\n- **TensorFlow/Keras**: Alternative framework by Google\n- **Hugging Face**: Pre-trained models and datasets\n- **Weights & Biases**: Experiment tracking\n- **TensorBoard**: Visualization toolkit\n\n---\n\n**Last Updated:** November 24, 2025  \n**Difficulty Level:** Intermediate",
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  {
    "text": "---\ntitle: \"How to Deploy Model to Arduino?\"\nid: \"arduinodeploy\"\ncategory: \"edge-ml\"\ntags: [\"edge machine learning\", \"\"]\nrelated: [\"arduino-edgeimpulse\"]\ndate: \"2021-11-28\"\ndescription: \"Deployment of edge impulse to Arduino\"\n---\n\n\nIn this post, I will explain the deployment of edge impulse to Arduino. \n\n\"You can deploy your impulse to any device. This makes the model run without an internet connection, \nminimizes latency, and runs with minimal power consumption.\"\n\nPlease check this website: [https://docs.edgeimpulse.com/docs/running-your-impulse-arduino](https://docs.edgeimpulse.com/docs/running-your-impulse-arduino)",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_0",
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  {
    "text": "- Go to your project from Edge Impulse, click Deployment.\n- Select Arduino from Deploy your impulse part. \n- Select Arduino Nano 33 BLE Sense from Build Firmware.\n- Select Quantized Version and click analyze. \n- Build your model.\n\nIt gives me arduino_motion-nano-33-ble-sense-v1.zip which includes flash.bat file. \nReboot arduino and run flash.bat.\nLunch command prompt run $edge-impulse-run --> This starts live classification.\n\nBut,  this is not what I want. I would like to **download impulse library.**\n\n    I click build again without selecting Arduino Nano 33 BLE Sense from Build Firmware, \n    it gives me ei-arduino-motion.zip this time.",
    "source": "2021-11-28-Deploy-To-Arduino.md",
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  {
    "text": "I click build again without selecting Arduino Nano 33 BLE Sense from Build Firmware, \n    it gives me ei-arduino-motion.zip this time. \n\n- Open Arduino IDE.\n- Sketch --> include licbrary --> Add zip --> select arduino-motion.zip\n- Tools --> board --> Arduino Mbed OS Nano Boards --> select Arduino Nano 33 BLE\n- File --> examples --> Arduino Motion Inferencing --> select Arduino Nano 33 BLE Sense Accelerometer\n- Tools --> Port --> Select Arduino COM\n- Click Upload  \n\nI encountered to this error:\n\n    Arduino_LSM9DS1.h: No such file or directory\n\nPlease check this website, I found it really heplpful: [https://www.programmingelectronics.com/no-such-file-error/](https://www.programmingelectronics.com/no-such-file-error/)",
    "source": "2021-11-28-Deploy-To-Arduino.md",
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    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_2",
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  },
  {
    "text": "I installed  Arduino_LSM9DS1  from: \n    \n    Open Arduino IDE: sketch --> include library --> manage libraries --> Arduino_LSM9DS1 \n\nUpload completed with the errors below:",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_3",
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    "text": "`C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\ConvolutionFunctions\\arm_convolve_HWC_q7_RGB.c: In function 'arm_convolve_HWC_q7_RGB':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\ConvolutionFunctions\\arm_convolve_HWC_q7_RGB.c:125:25: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n                             *__SIMD32(pBuffer) = 0x0;\n                             ^",
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    "text": "*__SIMD32(pBuffer) = 0x0;\n                             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\ConvolutionFunctions\\arm_convolve_HWC_q7_RGB.c:158:25: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n                             *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0);\n                             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_nn_mult_q15.c: In function 'arm_nn_mult_q15':",
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  {
    "text": "C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_nn_mult_q15.c:98:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_nn_mult_q15.c:99:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);\n             ^",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_6",
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  {
    "text": "*__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_nn_mult_q7.c: In function 'arm_nn_mult_q7':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_nn_mult_q7.c:80:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\NNSupportFunctions\\arm_q7_to_q15_reordered_no_shift.c: In function 'arm_q7_to_q15_reordered_no_shift':",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_7",
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    "text": "*__SIMD32(pDst)++ = in1;\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c: In function 'compare_and_replace_if_larger_q7':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c:78:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pIn)++ = in.word;\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c: In function 'accumulate_q7_to_q15':",
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  {
    "text": "C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c:122:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pCnt)++ = __QADD16(vo1, in);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c:125:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pCnt)++ = __QADD16(vo2, in);\n             ^",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_10",
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    "text": "*__SIMD32(pCnt)++ = __QADD16(vo2, in);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\dsp\\image\\processing.cpp: In function 'int ei::image::processing::yuv422_to_rgb888(unsigned char*, const unsigned char*, unsigned int, ei::image::processing::YUV_OPTIONS)':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\dsp\\image\\processing.cpp:73:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n         for (unsigned int i = 0; i < in_size_pixels; ++i) {\n                                  ~~^~~~~~~~~~~~~~~~",
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    "text": "for (unsigned int i = 0; i < in_size_pixels; ++i) {\n                                  ~~^~~~~~~~~~~~~~~~\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\tensorflow\\lite\\core\\api\\op_resolver.cpp: In function 'TfLiteStatus tflite::GetRegistrationFromOpCode(const tflite::OperatorCode*, const tflite::OpResolver&, tflite::ErrorReporter*, const TfLiteRegistration**)':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\tensorflow\\lite\\core\\api\\op_resolver.cpp:34:20: warning: comparison is always false due to limited range of data type [-Wtype-limits]\n           builtin_code < BuiltinOperator_MIN) {\n           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~",
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  {
    "text": "builtin_code < BuiltinOperator_MIN) {\n           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\n    \n    Sketch uses 153392 bytes (15%) of program storage space. Maximum is 983040 bytes.\n    Global variables use 48248 bytes (18%) of dynamic memory, leaving 213896 bytes for local variables. Maximum is 262144 bytes.\n    Device       : nRF52840-QIAA\n    Version      : Arduino Bootloader (SAM-BA extended) 2.0 [Arduino:IKXYZ]\n    Address      : 0x0\n    Pages        : 256\n    Page Size    : 4096 bytes\n    Total Size   : 1024KB\n    Planes       : 1\n    Lock Regions : 0\n    Locked       : none\n    Security     : false\n    Erase flash\n    \n    Done in 0.000 seconds\n    Write 153400 bytes to flash (38 pages)\n    [==============================] 100% (38/38 pages)\n    Done in 6.438 seconds`\n`",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_13",
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    "text": "Open Serial Monitor for Live Classification. \nDespite the errors, live classification is working well:)\n\n    Starting inferencing in 2 seconds...\n    Sampling...\n    Predictions (DSP: 21 ms., Classification: 0 ms., Anomaly: 0 ms.): \n        circle: 0.00000\n        idle: 0.03516\n        left-right: 0.00000\n        up-down: 0.96484\n    \n    Starting inferencing in 2 seconds...\n    Sampling...\n    Predictions (DSP: 20 ms., Classification: 0 ms., Anomaly: 0 ms.): \n        circle: 0.91406\n        idle: 0.00000\n        left-right: 0.08594\n        up-down: 0.00000\n    \n    Starting inferencing in 2 seconds...\n    Sampling...\n    Predictions (DSP: 20 ms., Classification: 0 ms., Anomaly: 0 ms.): \n        circle: 0.00000\n        idle: 0.99219\n        left-right: 0.00781\n        up-down: 0.00000",
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    "text": "Let's open the metadata:\n    \n    C:\\Users\\pelin\\Documents\\Arduino\\libraries -->  arduino_motion_inferencing --> src --> model_parameters\n\nOpen model_metadata.h.\n\nHere is the error: \n  \n    #error \"Cannot use full TensorFlow Lite with EON\"\n\nI installed  Arduino TensorflowLite via: \n\n    Open Arduino IDE: sketch --> include library --> manage libraries --> Arduino TensorflowLite. \n\nStart upload again. Same error :(",
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  {
    "text": "C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\CMSIS\\NN\\Source\\PoolingFunctions\\arm_pool_q7_HWC.c:125:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n             *__SIMD32(pCnt)++ = __QADD16(vo2, in);\n             ^\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\dsp\\image\\processing.cpp: In function 'int ei::image::processing::yuv422_to_rgb888(unsigned char*, const unsigned char*, unsigned int, ei::image::processing::YUV_OPTIONS)':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\dsp\\image\\processing.cpp:73:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_16",
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    "text": "for (unsigned int i = 0; i < in_size_pixels; ++i) {\n                                  ~~^~~~~~~~~~~~~~~~\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\tensorflow\\lite\\core\\api\\op_resolver.cpp: In function 'TfLiteStatus tflite::GetRegistrationFromOpCode(const tflite::OperatorCode*, const tflite::OpResolver&, tflite::ErrorReporter*, const TfLiteRegistration**)':\n    C:\\Users\\pelin\\Documents\\Arduino\\libraries\\arduino_motion_inferencing\\src\\edge-impulse-sdk\\tensorflow\\lite\\core\\api\\op_resolver.cpp:34:20: warning: comparison is always false due to limited range of data type [-Wtype-limits]\n           builtin_code < BuiltinOperator_MIN) {\n           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~",
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  {
    "text": "Yet, when I open:\n\n    File -> examples -> arduino_motion_inferencing -> Arduino Nano 33 BLE Sense Accelerometer Contionuous\n\nUpload and open serial monitor:",
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  {
    "text": "Predictions (DSP: 28 ms., Classification: 0 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 0 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 0 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_19",
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  },
  {
    "text": "Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): idle  [ 0, 10, 0, 0, 0, 0, ]\n    Predictions (DSP: 28 ms., Classification: 0 ms., Anomaly: 0 ms.): uncertain  [ 3, 0, 4, 0, 3, 0, ]\n    Predictions (DSP: 26 ms., Classification: 0 ms., Anomaly: 0 ms.): uncertain  [ 3, 0, 4, 0, 3, 0, ]\n    Predictions (DSP: 26 ms., Classification: 1 ms., Anomaly: 0 ms.): uncertain  [ 4, 0, 4, 0, 2, 0, ]\n    Predictions (DSP: 28 ms., Classification: 0 ms., Anomaly: 0 ms.): uncertain  [ 5, 0, 4, 0, 1, 0, ]\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): uncertain  [ 6, 0, 3, 0, 1, 0, ]\n    ....\n    Predictions (DSP: 28 ms., Classification: 1 ms., Anomaly: 0 ms.): up-down  [1, 0, 1, 8, 0, 0, ]",
    "source": "2021-11-28-Deploy-To-Arduino.md",
    "type": "markdown",
    "chunk_id": "2021-11-28-Deploy-To-Arduino.md_20",
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  {
    "text": "No errors!\n\nFirst 4 numbers in the list represents:\n- 1: circle\n- 2: idle \n- 3: left_right\n- 4: up_down",
    "source": "2021-11-28-Deploy-To-Arduino.md",
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  {
    "text": "## Key Words\nTiny ML, Arduino, Edge Impulse, Deployment",
    "source": "2021-11-28-Deploy-To-Arduino.md",
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  {
    "text": "---\ntitle: \"NER\"\nid: \"ner\"\ncategory: \"deep-learning\"\ntags: [\"deep learning\", \"dl\", \"ner\", \"from scratch\", \"medium\"]\nrelated: [\"dl\"]\ndate: \"2023-08-16\"\ndescription: \"Build your NER data from scratch and learn the details of the NER model\"\n---\n\n\n[Read the full version on Medium](https://medium.com/@balci.pelin/ner-8eb9694ccad3) \n\nBuild your NER data from scratch and learn the details of the NER model.",
    "source": "2023-08-16-NER.md",
    "type": "markdown",
    "chunk_id": "2023-08-16-NER.md_0",
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    "text": "FFT_PSD\n\nFast Fourier Transform & Spectral Analysis&#182;\n\nThis month, I took a lecture from Coursera, Introduction to Embedded Machine Learning. In this course, we use the Arduino board to train audio and motion data. This is the first time I dealt with audio & vibration analysis. As you guess, the critical part of the training model is feature extraction. Thanks to edge impulse, I didn't need to deal with feature extraction during the project, since it analyses the data automatically and chooses the right data for the model. However, I don't want to use data that I have no idea about, therefore, I've searched fast Fourier transform and power spectral density.",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
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    "text": "Below you will find codes & notes from various platforms (references are presented at the end of the notebook). Hope that this notebook will be a good start for you as well.\n\nFFT&#182;\n\nFourier transform outputs vibration amplitude as a function of frequency so that the analyzer can understand what is causing the vibration.\n\nThe frequency resolution in an FFT is directly proportional to the signal length and sample rate. [3]\n\nSpectrogram&#182;\n\nA spectrogram takes a series of FFTs and overlaps them to illustrate how the spectrum (frequency domain) changes with time. [3]\n\nPSD&#182;\n\nA power spectral density (PSD) takes the amplitude of the FFT, multiplies it by its complex conjugate and normalizes it to the frequency bin width.",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
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  {
    "text": "A power spectral density (PSD) takes the amplitude of the FFT, multiplies it by its complex conjugate and normalizes it to the frequency bin width.\n\nThis allows for accurate comparison of random vibration signals that have different signal lengths. [3]\n\nNotes&#182;\n\nwave length is length of one cycle. distance/number of cycles\n\namplitude is median value fo highest and lowest y values in a cycle\n\nperiod = time/number of cycles --> T\nfrequency = number of cycles / time --> 1/T --> 1/s --> s^-1 --> Hz\ni.e freq is number of cycles in a second. [1]\n\nRelationships\n\nwave length inc & freq dec & period inc & Energy dec\n\nwavelength ~ period (directly related)\n\nfreq ~ Energy (directly related) [1]\n\nSmall example&#182;\n\nIn [1]:\n\nperiod = 0.25\nfreq = 1/0.25\nfreq # --> 4 cycles in a second --> 4 Hz",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_2",
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    "text": "freq ~ Energy (directly related) [1]\n\nSmall example&#182;\n\nIn [1]:\n\nperiod = 0.25\nfreq = 1/0.25\nfreq # --> 4 cycles in a second --> 4 Hz\n\nOut[1]:\n\n4.0\n\nIn [2]:\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nExample 1 -FFT [4]&#182;\n\nIn [3]:\n\nsampling_freq = 100 # time points\nsampling_interval = 1/sampling_freq\nbegin_time = 0\nend_time = 10\n\nsignal_1_freq = 2\nsignal_2_freq = 3\n\ntime = np.arange(begin_time, end_time, sampling_interval)\nprint(time[:10])\n\n[0. 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09]\n\nIn [4]:\n\n# create sin waves\nwave1 = np.sin(2*np.pi*signal_1_freq*time)\nwave2 = np.sin(2*np.pi*signal_2_freq*time)\n\nIn [5]:\n\nplt.figure(figsize=(12,5))\nplt.plot(time, wave1)\n\nplt.figure(figsize=(12,5))\nplt.plot(time, wave2)\nplt.show()\n\nIn [6]:\n\n# Add the sine waves",
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    "text": "plt.figure(figsize=(12,5))\nplt.plot(time, wave1)\n\nplt.figure(figsize=(12,5))\nplt.plot(time, wave2)\nplt.show()\n\nIn [6]:\n\n# Add the sine waves\n\nwave = wave1 + wave2\nplt.figure(figsize=(12,5))\nplt.plot(time, wave)\nplt.show()\n\nIn [7]:\n\n# Frequency domain representation\n\nfourierTransform = np.fft.fft(wave)/len(wave) # Normalize amplitude\nfourierTransform = fourierTransform[range(int(len(wave)/2))] # Exclude sampling frequency\n\ntpCount = len(wave)\nvalues = np.arange(int(tpCount/2))\ntimePeriod = tpCount/sampling_freq\nfrequencies = values/timePeriod\n\nIn [8]:\n\nplt.figure(2)\nplt.title('FFT')\nplt.plot(frequencies, abs(fourierTransform))\nplt.axis([0, 6.5, 0, 0.5])\nplt.show()\n\nExample 2 - Waves - FFT - PSD [2]&#182;\n\nIn [9]:\n\nn = 1000 # number of points\nLx = 100 # time period\n\nx = np.linspace(0,Lx,n)",
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  {
    "text": "Example 2 - Waves - FFT - PSD [2]&#182;\n\nIn [9]:\n\nn = 1000 # number of points\nLx = 100 # time period\n\nx = np.linspace(0,Lx,n)\n\nprint(x[:10])\n\n[0. 0.1001001 0.2002002 0.3003003 0.4004004 0.5005005 0.6006006\n0.7007007 0.8008008 0.9009009]\n\nIn [10]:\n\ny1 = 1*np.cos((2*np.pi/Lx)*x) # 1 is amplitude, 1 wave\ny2 = 2*np.sin((2*np.pi/Lx)*x) # 2 is amplitude, 1 wave\ny3 = 0.5*np.cos(3*(2*np.pi/Lx)*x) # 0.5 is amplitude, 3 waves\ny4 = 0.5*np.sin(5*(2*np.pi/Lx)*x) # 0.5 is amplitude, 5 waves\n\nprint(y1[:10])\nplt.plot(x,y1)\nplt.title('cos')\nplt.show()\n\nplt.plot(x,y3)\nplt.title('5cos')\nplt.show()\n\nplt.plot(x,y2)\nplt.title('sin')\nplt.show()\n\nplt.plot(x,y4)\nplt.title('10sin')\nplt.show()\n\n[1. 0.99998022 0.99992089 0.999822 0.99968356 0.99950557\n0.99928805 0.999031 0.99873443 0.99839835]\n\nIn [11]:",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_5",
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    "text": "[1. 0.99998022 0.99992089 0.999822 0.99968356 0.99950557\n0.99928805 0.999031 0.99873443 0.99839835]\n\nIn [11]:\n\ny = y1 + y2 + y3 + y4\nplt.plot(x,y)\nplt.show()\n\nIn [12]:\n\nfreqs = np.fft.fftfreq(n)\nfft_vals = np.fft.fft(y) # FFT values\nfft_theo = 2*np.abs(fft_vals/n) # True FFT\n\nIn [13]:\n\nplt.figure(1)\nplt.title('Original')\nplt.plot(x,y)\n\nplt.figure(2)\nplt.title('FFT')\nplt.plot(abs(freqs), abs(fft_theo))\nplt.axis([-0.01, 0.02, 0, 2.5])\nplt.show()\n\nplt.psd(y, 5000, 1 / sampling_freq)\nplt.show()\n\nExample 3 - PSD [5]&#182;\n\nIn [14]:\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.gridspec as gridspec\n\nIn [15]:",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
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  {
    "text": "In [14]:\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.gridspec as gridspec\n\nIn [15]:\n\nsampling_freq = 0.01\nbegin_time = 0\nend_time = 10\ntime = np.arange(begin_time, end_time, sampling_interval)\nwave_ = 0.1 * np.sin(2 * np.pi * time)\n\nfig, (ax0, ax1, ax2) = plt.subplots(3, 1, figsize=(12,7))\nax0.plot(time, wave_)\nax1.psd(wave_, 512, 1 / sampling_freq)\nax2.specgram(wave_, Fs=sampling_freq)\n\nplt.show()\n\nAdd noise\n\nIn [16]:\n\nsampling_freq = 0.01\nbegin_time = 0\nend_time = 10\ntime = np.arange(begin_time, end_time, sampling_interval)\n\n# noise\nnp.random.seed(42)\nrandom_values = np.random.randn(len(time))\nexp_values = np.exp(-time / 0.05)\nnoise = np.convolve(random_values, exp_values) * sampling_freq\nnoise = noise[:len(time)]",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
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  {
    "text": "wave_ = 0.1 * np.sin(2 * np.pi * time) + noise\n\nfig, (ax0, ax1, ax2) = plt.subplots(3, 1, figsize=(12,7))\nax0.plot(time, wave_)\nax1.psd(wave_, 512, 1 / sampling_freq)\nax2.specgram(wave_, Fs=sampling_freq)\n\nplt.show()\n\nExample 6 - Spectogram [7]&#182;\n\nIn [17]:\n\n# import the libraries\nimport matplotlib.pyplot as plot\nimport numpy as np\n\n# Define the list of frequencies\n\nfrequencies= np.arange(5,105,5)\n\n# Sampling Frequency\nsamplingFrequency = 400\n\n# Create two ndarrays\ns1 = np.empty([0]) # For samples\ns2 = np.empty([0]) # For signal\n\n# Start - stop Value of the sample\nstart = 1\nstop = samplingFrequency+1",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_8",
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  {
    "text": "# Start - stop Value of the sample\nstart = 1\nstop = samplingFrequency+1\n\nfor frequency in frequencies:\nsub1 = np.arange(start, stop, 1)\n# Signal - Sine wave with varying frequency + Noise\nsub2 = np.sin(2*np.pi*sub1*frequency*1/samplingFrequency)+np.random.randn(len(sub1))\ns1 = np.append(s1, sub1)\ns2 = np.append(s2, sub2)\nstart = stop+1\nstop = start+samplingFrequency\n\n# Plot the signal\n\nplot.subplot(211)\nplot.plot(s1,s2)\nplot.xlabel('Sample')\nplot.ylabel('Amplitude')\n\n# Plot the spectrogram\n\nplot.subplot(212)\npowerSpectrum, freqenciesFound, time, imageAxis = plot.specgram(s2, Fs=samplingFrequency)\nplot.xlabel('Time')\nplot.ylabel('Frequency')\nplot.show()\n\nprint(powerSpectrum.shape)\nprint(powerSpectrum[0][:10])",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_9",
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  {
    "text": "print(powerSpectrum.shape)\nprint(powerSpectrum[0][:10])\n\n(129, 61)\n[4.65115394e-03 2.30334436e-04 2.40199530e-04 2.62529327e-03\n4.42714257e-03 1.74208829e-04 5.96734856e-05 3.74071934e-04\n2.43757906e-04 1.12785127e-03]\n\nReferences:\n\n• https://www.youtube.com/watch?v=DSz4ZILAneQ [1]\n•\n\n• https://www.youtube.com/watch?v=su9YSmwZmPg [2]\n•\n\n• https://blog.endaq.com/vibration-analysis-fft-psd-and-spectrogram [3]\n-https://pythontic.com/visualization/signals/fouriertransform_fft [4]\n•\n\n• https://matplotlib.org/stable/gallery/lines_bars_and_markers/psd_demo.html [5]\n•\n\n• https://fairyonice.github.io/implement-the-spectrogram-from-scratch-in-python.html [6]\n•\n\n• https://pythontic.com/visualization/signals/spectrogram [7]\n•",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_10",
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  {
    "text": "• https://pythontic.com/visualization/signals/spectrogram [7]\n•\n\n• https://analyticsindiamag.com/hands-on-tutorial-on-visualizing-spectrograms-in-python/\n•\n\nIn [19]:\n\n# !pip install nbconvert\n#!jupyter nbconvert --to html FFT_PSD.ipynb",
    "source": "notebook: 2021-12-19-Fast Fourier Tranform.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2021-12-19-Fast Fourier Tranform.html_11",
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  {
    "text": "deep_dream_tutorial\n\nDeep Dream from Scratch with PyTorch¶\n\nUnderstanding How Neural Networks \"See\" Through Feature Visualization\n\nDeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images. [https://en.wikipedia.org/wiki/DeepDream]\n\nDeep Dream was introduced in 2015 and quickly became famous for its surreal, psychedelic images filled with swirling patterns, eyes, and dog faces. But beyond the trippy visuals, Deep Dream teaches us something fundamental: how neural networks perceive and represent visual information.",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_0",
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    "text": "In this tutorial, we'll build Deep Dream from scratch using PyTorch. By the end, you'll understand:\n\n• How convolutional neural networks detect features at different layers\n•\n\n• How to use gradients to modify an image (instead of modifying model weights)\n•\n\n• Why Deep Dream images look the way they do\n•\n\n• How this connects to modern AI interpretability research\n•\n\nThe Core Idea:¶\n\nNormal use of a trained network:\n\nImage goes in → network detects features layer by layer (edges → shapes → textures → objects) → outputs a label like \"dog\"\nThe image is just read, never changed.\n\nDeep Dream flips the goal:\n\nInstead of asking \"what's in this image?\", you ask: \"Change this image so the patterns you're detecting become stronger.\"\n\nHere's the actual process:",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_1",
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    "text": "Here's the actual process:\n\n• You feed your image into the pre-trained network\n•\n\n• You pick a layer in the network (each layer detects different things—early layers see edges, deeper layers see complex objects like faces or animals)\n•\n\n• The network looks at that layer's activations (how strongly it's detecting patterns)\n•\n\n• Now the key part: you calculate how to tweak each pixel in your original image to make those activations stronger\n•\n\n• You slightly modify the image pixels accordingly\n•\n\n• Repeat steps 1–5 many times\n•\n\nEach iteration, the image gets nudged to contain \"more\" of whatever that layer is detecting. After many loops, faint patterns snowball into vivid, exaggerated features.\n\nWe don't train a model.\n\nWe take a pre-trained model and freeze it.",
    "source": "notebook: deep_dream_tutorial.html",
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  {
    "text": "We don't train a model.\n\nWe take a pre-trained model and freeze it.\n\nWe forward pass an image through the model and capture the activations at a chosen layer.\n\nThen we compute gradients of those activations with respect to the input image pixels.\n\nFinally, we add these gradients back to the image—this amplifies whatever patterns the layer detected. Repeat this process, and the patterns snowball into the trippy Deep Dream effect.\n\n1. Setup and Imports¶\n\nWe'll use a pre-trained InceptionV3 model. This is the same architecture Google used in the original Deep Dream.\n\nIn [1]:\n\n# Install dependencies if needed\n!pip install torch torchvision pillow matplotlib numpy",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_3",
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    "text": "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch) (3.5.0)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.61.1)\nRequirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (25.0)",
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    "text": "In [2]:\n\nimport torch\nimport torch.nn as nn\nfrom torchvision import models, transforms\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom typing import List, Tuple, Optional\nimport requests\nfrom io import BytesIO\n\n# Check for GPU\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f\"Using device: {device}\")\n\nUsing device: cuda\n\n2. Understanding the Core Idea¶\n\nIn normal neural network usage:\n\n• Input image → Network → Output prediction\n•\n\n• We adjust the network weights to improve predictions\n•\n\nIn Deep Dream:\n\n• Input image → Network → Layer activations\n•\n\n• We adjust the input image pixels to amplify those activations\n•",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_11",
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    "text": "In Deep Dream:\n\n• Input image → Network → Layer activations\n•\n\n• We adjust the input image pixels to amplify those activations\n•\n\nThe key insight: we compute gradients with respect to the input image, not the weights. Then we add those gradients to the image to make the detected patterns stronger.\n\n3. Load the Pre-trained Model¶\n\nWe'll use InceptionV3 and create a wrapper that lets us extract activations from any layer.\n\nIn [3]:\n\nclass DeepDreamModel(nn.Module):\n\"\"\"\nWrapper around InceptionV3 that captures intermediate layer activations.\n\"\"\"\ndef __init__(self):\nsuper().__init__()\n# Load pre-trained InceptionV3\nself.inception = models.inception_v3(weights=models.Inception_V3_Weights.DEFAULT)\nself.inception.eval()",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_12",
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  },
  {
    "text": "# Freeze all parameters\nfor param in self.inception.parameters():\nparam.requires_grad = False\n\n# Dictionary to store layer outputs\nself.layer_outputs = {}\nself.hooks = []\n\n# Register hooks on the actual module structure\nself._register_hooks()\n\ndef _register_hooks(self):\n\"\"\"Register forward hooks on layers we want to visualize.\"\"\"",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_13",
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  {
    "text": "def _register_hooks(self):\n\"\"\"Register forward hooks on layers we want to visualize.\"\"\"\n\n# Get actual layer references from the model\ntarget_layers = {\n'Conv2d_1a': self.inception.Conv2d_1a_3x3,\n'Conv2d_2b': self.inception.Conv2d_2b_3x3,\n'Mixed_5b': self.inception.Mixed_5b,\n'Mixed_5c': self.inception.Mixed_5c,\n'Mixed_5d': self.inception.Mixed_5d,\n'Mixed_6a': self.inception.Mixed_6a,\n'Mixed_6b': self.inception.Mixed_6b,\n'Mixed_6c': self.inception.Mixed_6c,\n'Mixed_6d': self.inception.Mixed_6d,\n'Mixed_6e': self.inception.Mixed_6e,\n'Mixed_7a': self.inception.Mixed_7a,\n'Mixed_7b': self.inception.Mixed_7b,\n'Mixed_7c': self.inception.Mixed_7c,\n}\n\nfor name, layer in target_layers.items():\nhook = layer.register_forward_hook(self._make_hook(name))\nself.hooks.append(hook)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_14",
    "embedding": [
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  {
    "text": "for name, layer in target_layers.items():\nhook = layer.register_forward_hook(self._make_hook(name))\nself.hooks.append(hook)\n\n# Store the layer names so we can report them\nself._layer_names = list(target_layers.keys())\n\ndef _make_hook(self, name):\n\"\"\"Create a hook function that saves the layer output.\"\"\"\ndef hook(module, input, output):\nself.layer_outputs[name] = output\nreturn hook\n\ndef forward(self, x):\n\"\"\"Forward pass - this populates self.layer_outputs.\"\"\"\nself.layer_outputs = {}\n_ = self.inception(x)\nreturn self.layer_outputs\n\ndef get_available_layers(self):\n\"\"\"Return list of layers we can dream on.\"\"\"\nreturn self._layer_names\n\n# Initialize the model\nmodel = DeepDreamModel().to(device)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_15",
    "embedding": [
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  {
    "text": "# Initialize the model\nmodel = DeepDreamModel().to(device)\n\n# Run a dummy forward pass to verify hooks work\ndummy = torch.randn(1, 3, 299, 299).to(device)\n_ = model(dummy)\nprint(f\"Model loaded!\")\nprint(f\"Available layers for dreaming: {model.get_available_layers()}\")\nprint(f\"Hooks captured {len(model.layer_outputs)} layers\")\n\nDownloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n\n100%|██████████| 104M/104M [00:00<00:00, 177MB/s]\n\nModel loaded!\nAvailable layers for dreaming: ['Conv2d_1a', 'Conv2d_2b', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']\nHooks captured 13 layers\n\nWhat is hook?",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_16",
    "embedding": [
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    "text": "What is hook?\n\nA hook is a way to \"spy on\" what's happening inside a neural network during the forward pass.\nNormally, when you run a model:\n\nInput → Layer1 → Layer2 → Layer3 → ... → Output\n↑\n(intermediate values are computed\nbut you never see them)\n\nWith a hook, you can intercept those intermediate values:\n\nInput → Layer1 → Layer2 → Layer3 → ... → Output\n│\n├──→ Hook captures this!\n│ (saved to a variable)\n\nWhy we need hooks for Deep Dream:\n\n• We need the activations from a middle layer (not the final output)\n•\n\n• We want to compute gradients with respect to those activations\n•\n\n• Hooks let us \"reach inside\" and grab those values\n•\n\nLet's look at the model:\n\nIn [4]:\n\nmodel\n\nOut[4]:",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_17",
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    "text": "DeepDreamModel(\n(inception): Inception3(\n(Conv2d_1a_3x3): BasicConv2d(\n(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(Conv2d_2a_3x3): BasicConv2d(\n(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(Conv2d_2b_3x3): BasicConv2d(\n(conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n(Conv2d_3b_1x1): BasicConv2d(",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_18",
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    "text": "(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n)\n(Mixed_6a): InceptionB(\n(branch3x3): BasicConv2d(\n(conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch3x3dbl_1): BasicConv2d(\n(conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch3x3dbl_2): BasicConv2d(\n(conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch3x3dbl_3): BasicConv2d(\n(conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)",
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    "text": ")\n(branch7x7dbl_5): BasicConv2d(\n(conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch_pool): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n)\n(Mixed_6d): InceptionC(\n(branch1x1): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch7x7_1): BasicConv2d(\n(conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)",
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    "text": ")\n(branch7x7_1): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch7x7_2): BasicConv2d(\n(conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch7x7_3): BasicConv2d(\n(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch7x7dbl_1): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)",
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    "text": ")\n(conv1): BasicConv2d(\n(conv): Conv2d(128, 768, kernel_size=(5, 5), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(fc): Linear(in_features=768, out_features=1000, bias=True)\n)\n(Mixed_7a): InceptionD(\n(branch3x3_1): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch3x3_2): BasicConv2d(\n(conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)\n(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n)\n(branch7x7x3_1): BasicConv2d(\n(conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)",
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    "text": "Mixed_5c is not a single layer—it's an Inception module, which is a mini-network with multiple parallel branches.\nHere's what's happening inside Mixed_5c:\n\nInput\n│\n┌──────────────┼──────────────┬──────────────┐\n│ │ │ │\n▼ ▼ ▼ ▼\nbranch1x1 branch5x5 branch3x3dbl branch_pool\n(1x1 conv) (1x1 → 5x5) (1x1 → 3x3 → 3x3) (pool → 1x1)\n│ │ │ │\n└──────────────┴──────────────┴──────────────┘\n│\nConcatenate\n│\nOutput\n\nThe key insight of the Inception architecture is: we don't know which filter size is best for detecting a feature, so let's use multiple sizes in parallel.",
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  {
    "text": "branch1x1: Captures fine-grained, pixel-level features\nbranch5x5: Captures medium-sized patterns (done as 1x1 → 5x5 to reduce computation)\nbranch3x3dbl: Captures larger patterns via stacked 3x3 convolutions\nbranch_pool: Preserves spatial information through pooling\n\nAll four branches process the same input, then their outputs are concatenated along the channel dimension.\n\n4. Image Preprocessing¶\n\nWe need to:\n\n• Normalize the image for InceptionV3 (it expects specific mean/std)\n•\n\n• Be able to convert back for display\n•\n\nIn [5]:\n\n# ImageNet normalization values\nIMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)\nIMAGENET_STD = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_50",
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  {
    "text": "def load_image(path_or_url: str, max_size: int = 512) -> torch.Tensor:\n\"\"\"\nLoad an image from a file path or URL.\nReturns a normalized tensor ready for the model.\n\"\"\"\n# Load image\nif path_or_url.startswith('http'):\nheaders = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}\nresponse = requests.get(path_or_url, headers=headers, allow_redirects=True)\nresponse.raise_for_status()\nimg = Image.open(BytesIO(response.content)).convert('RGB')\nelse:\nimg = Image.open(path_or_url).convert('RGB')\n\n# Resize while maintaining aspect ratio\nratio = max_size / max(img.size)\nnew_size = tuple(int(dim * ratio) for dim in img.size)\nimg = img.resize(new_size, Image.LANCZOS)\n\n# Convert to tensor [0, 1]\nimg_tensor = transforms.ToTensor()(img).unsqueeze(0).to(device)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_51",
    "embedding": [
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  },
  {
    "text": "# Convert to tensor [0, 1]\nimg_tensor = transforms.ToTensor()(img).unsqueeze(0).to(device)\n\n# Normalize for InceptionV3\nimg_tensor = (img_tensor - IMAGENET_MEAN) / IMAGENET_STD\n\nreturn img_tensor\n\ndef tensor_to_image(tensor: torch.Tensor) -> np.ndarray:\n\"\"\"\nConvert a normalized tensor back to a displayable image.\n\"\"\"\n# Denormalize\nimg = tensor * IMAGENET_STD + IMAGENET_MEAN\n\n# Clip to valid range and convert\nimg = img.squeeze(0).permute(1, 2, 0).cpu().detach().numpy()\nimg = np.clip(img, 0, 1)\n\nreturn img\n\ndef show_image(tensor: torch.Tensor, title: str = \"\"):\n\"\"\"Display a tensor as an image.\"\"\"\nimg = tensor_to_image(tensor)\nplt.figure(figsize=(10, 10))\nplt.imshow(img)\nplt.title(title)\nplt.axis('off')\nplt.show()\n\n5. The Core Deep Dream Algorithm¶",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_52",
    "embedding": [
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  {
    "text": "5. The Core Deep Dream Algorithm¶\n\nHere's where the magic happens. The algorithm is surprisingly simple:\n\nfor each iteration:\n1. Forward pass: get layer activations\n2. Compute loss: mean of activations (we want to maximize this)\n3. Backward pass: compute gradients with respect to input image\n4. Update image: add gradients to amplify detected patterns\n\nIn [6]:\n\ndef deep_dream_step(\nmodel: DeepDreamModel,\nimage: torch.Tensor,\nlayer_name: str,\nlearning_rate: float = 0.01\n) -> torch.Tensor:\n\"\"\"\nPerform a single Deep Dream optimization step.\n\nArgs:\nmodel: The DeepDream model wrapper\nimage: Input image tensor (requires_grad must be True)\nlayer_name: Which layer to maximize activations for\nlearning_rate: Step size for gradient ascent",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_53",
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  {
    "text": "Returns:\nUpdated image tensor\n\"\"\"\n# Forward pass\nlayer_outputs = model(image)\n\n# Get the activations from our target layer\nactivations = layer_outputs[layer_name]\n\n# Loss = mean of activations\n# We want to MAXIMIZE this, so we'll do gradient ASCENT\nloss = activations.mean()\n\n# Backward pass - compute gradients w.r.t. image\nloss.backward()\n\n# Normalize gradients (helps stabilize the process)\ngrad = image.grad.data\ngrad = grad / (grad.std() + 1e-8) # Normalize by standard deviation\n\n# Gradient ASCENT (add gradients, not subtract)\nimage.data = image.data + learning_rate * grad\n\n# Clear gradients for next iteration\nimage.grad.data.zero_()\n\nreturn image",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_54",
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  {
    "text": "# Clear gradients for next iteration\nimage.grad.data.zero_()\n\nreturn image\n\ndef deep_dream(\nmodel: DeepDreamModel,\nimage: torch.Tensor,\nlayer_name: str,\niterations: int = 20,\nlearning_rate: float = 0.01,\nshow_progress: bool = True\n) -> torch.Tensor:\n\"\"\"\nRun the Deep Dream algorithm.\n\nArgs:\nmodel: The DeepDream model wrapper\nimage: Input image tensor\nlayer_name: Which layer to dream on\niterations: Number of optimization steps\nlearning_rate: Step size\nshow_progress: Whether to print progress\n\nReturns:\nDreamed image tensor\n\"\"\"\n# Clone image and enable gradients\ndream_image = image.clone().requires_grad_(True)\n\nfor i in range(iterations):\ndream_image = deep_dream_step(model, dream_image, layer_name, learning_rate)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_55",
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  {
    "text": "for i in range(iterations):\ndream_image = deep_dream_step(model, dream_image, layer_name, learning_rate)\n\nif show_progress and (i + 1) % 5 == 0:\nprint(f\"Iteration {i + 1}/{iterations}\")\n\nreturn dream_image.detach()\n\nprint(\"Deep Dream functions defined!\")\n\nDeep Dream functions defined!\n\nDetails of Deep Dream¶\n\n1. Gradient Ascent vs Gradient Descent\n\nNormal training (Gradient Descent):\n\nGoal: minimize loss (make predictions better)\nUpdate: weights = weights - learning_rate * gradient\nWe go downhill on the loss landscape\n\nDeep Dream (Gradient Ascent):\n\nGoal: maximize activations (make patterns stronger)\nUpdate: image = image + learning_rate * gradient\nWe go uphill on the activation landscape\n\nLoss Landscape:\n\n╱╲\n╱ ╲\n╱ ╲ Descent: go DOWN to find minimum\n╱ ╲ (subtract gradient)\n╱ ↓ ╲\nminimum",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_56",
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  {
    "text": "Loss Landscape:\n\n╱╲\n╱ ╲\n╱ ╲ Descent: go DOWN to find minimum\n╱ ╲ (subtract gradient)\n╱ ↓ ╲\nminimum\n\nActivation Landscape:\n\nmaximum\n↑ Ascent: go UP to find maximum\n╱ ↑ ╲ (add gradient)\n╱ ╲\n╱ ╲\n\n2. Gradient Normalization\n\nIn normal training:\n\nWe don't usually normalize gradients this way. We just use them directly (or with optimizers like Adam that do their own scaling).\n\nIn Deep Dream:\n\n# Normalize gradients (helps stabilize the process)\ngrad = image.grad.data\ngrad = grad / (grad.std() + 1e-8) # Normalize by standard deviation\n\nWe normalize because:\n\nGradients can vary wildly in magnitude across different images/layers\nWithout normalization, some iterations might make huge changes, others tiny\nDividing by std() makes each step roughly the same size\n\n3. Image.grad?",
    "source": "notebook: deep_dream_tutorial.html",
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  {
    "text": "3. Image.grad?\n\n# Before backward():\nimage.data = [[0.5, 0.3], [0.8, 0.2]] # pixel values\nimage.grad = None # no gradients yet\n\n# After loss.backward():\nimage.data = [[0.5, 0.3], [0.8, 0.2]] # unchanged\nimage.grad = [[0.02, -0.01], [0.05, 0.03]] # now populated!\n\n# After gradient ascent update:\nimage.data = [[0.52, 0.29], [0.85, 0.23]] # modified!\nimage.grad = [[0.02, -0.01], [0.05, 0.03]] # still there\n\n# After zero_():\nimage.data = [[0.52, 0.29], [0.85, 0.23]] # unchanged\nimage.grad = [[0.0, 0.0], [0.0, 0.0]] # cleared for next iteration\n\n6. Let's Dream!¶\n\nNow let's try it out. We'll use a sample image and see what different layers produce.\n\nIn [7]:\n\n# Sky with clouds (patterns emerge beautifully)\nsample_url = \"https://images.unsplash.com/photo-1534088568595-a066f410bcda?w=600\"",
    "source": "notebook: deep_dream_tutorial.html",
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  {
    "text": "In [7]:\n\n# Sky with clouds (patterns emerge beautifully)\nsample_url = \"https://images.unsplash.com/photo-1534088568595-a066f410bcda?w=600\"\n\noriginal_image = load_image(sample_url, max_size=400)\nshow_image(original_image, \"Original Image\")\n\nIn [8]:\n\n# Dream on an early layer (edges and simple patterns)\nearly_dream = deep_dream(\nmodel,\noriginal_image,\nlayer_name='Mixed_5b', # Early-mid layer\niterations=30,\nlearning_rate=0.01\n)\nshow_image(early_dream, \"Deep Dream - Mixed_5b (Early Layer: Textures & Patterns)\")\n\nIteration 5/30\nIteration 10/30\nIteration 15/30\nIteration 20/30\nIteration 25/30\nIteration 30/30\n\nIn [9]:",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_59",
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  {
    "text": "Iteration 5/30\nIteration 10/30\nIteration 15/30\nIteration 20/30\nIteration 25/30\nIteration 30/30\n\nIn [9]:\n\n# Dream on a deeper layer (object parts and complex features)\ndeep_dream_result = deep_dream(\nmodel,\noriginal_image,\nlayer_name='Mixed_6c', # Deeper layer\niterations=30,\nlearning_rate=0.01\n)\nshow_image(deep_dream_result, \"Deep Dream - Mixed_6c (Deep Layer: Object Parts)\")\n\nIteration 5/30\nIteration 10/30\nIteration 15/30\nIteration 20/30\nIteration 25/30\nIteration 30/30\n\nIn [10]:\n\n# Dream on the deepest layer (full objects - eyes, faces, animals)\ndeepest_dream = deep_dream(\nmodel,\noriginal_image,\nlayer_name='Mixed_7c', # Deepest layer\niterations=30,\nlearning_rate=0.01\n)\nshow_image(deepest_dream, \"Deep Dream - Mixed_7c (Deepest Layer: Complex Objects)\")",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_60",
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    "text": "Iteration 5/30\nIteration 10/30\nIteration 15/30\nIteration 20/30\nIteration 25/30\nIteration 30/30\n\n7. Multi-Scale Deep Dream (Octaves)¶\n\nThe basic algorithm works, but the results can look noisy. A common improvement is multi-scale processing (also called \"octaves\"):\n\n• Start with a small version of the image\n•\n\n• Dream on it\n•\n\n• Upscale the result\n•\n\n• Add details from the original image\n•\n\n• Dream again\n•\n\n• Repeat\n•\n\nThis creates more coherent, visually appealing results.\n\n1. Octave\n\nProcess at multiple scales (small → large), like looking at an image from far away, then closer, then closer.\n\nnum_octaves = 4\noctave_scale = 1.4\noriginal image = 512x512",
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    "text": "num_octaves = 4\noctave_scale = 1.4\noriginal image = 512x512\n\nOctave sizes (small to large):\noctave 0: 512 / 1.4³ = 186x186 (far away - big patterns)\noctave 1: 512 / 1.4² = 261x261\noctave 2: 512 / 1.4¹ = 365x365\noctave 3: 512 / 1.4⁰ = 512x512 (close up - fine details)\n\nWhy?\n\n• Small scale → network sees the whole image → creates large, coherent patterns\n•\n\n• Large scale → network sees details → adds fine texture\n•\n\n2. What is interpolate? Why do we need it?\n\ninterpolate = resize an image\n\n•\nOriginal: 512x512\n\n•\n\n•\nWe want: 186x186\n\nscaled_image = torch.nn.functional.interpolate(\noriginal, # input tensor\nsize=(186, 186), # target size\nmode='bilinear', # smooth resizing (not blocky)\nalign_corners=False\n)\n\n•\n\nVisual example:",
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  {
    "text": "•\n\nVisual example:\n\n```\nOriginal (512x512): Interpolated (186x186):\n┌─────────────────┐ ┌───────┐\n│ │ │ │\n│ 🐱 │ ──→ │ 🐱 │\n│ │ │ │\n│ │ └───────┘\n└─────────────────┘ (same image, smaller)\n\nWhy needed? The model can only dream at one size at a time. We resize to process at different scales.\n\n5. Why extract detail?\n\nWe want to accumulate only the new patterns, not the original image:\npython\n\ndetail = dreamed - scaled_image\n\n# ↑ ↑\n# output input (without previous detail)\n#\n# = only the NEW patterns added by this octave\n\nVisual example:\n\nscaled_image: dreamed: detail:\n┌───────┐ ┌───────┐ ┌───────┐\n│ │ │ ~∿∿~ │ │ ~∿∿~ │\n│ 🐱 │ + │ 🐱 │ = │ │ (just the swirls)\n│ │ dream │ ~∿∿~ │ - │ ~∿∿~ │\n└───────┘ └───────┘ └───────┘",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_63",
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    "text": "Why? So we can upscale just the patterns and add more detail in the next octave, without duplicating the base image.\n\nIn [11]:\n\ndef deep_dream_octaves(\nmodel: DeepDreamModel,\nimage: torch.Tensor,\nlayer_name: str,\nnum_octaves: int = 4,\noctave_scale: float = 1.4,\niterations_per_octave: int = 100,\nlearning_rate: float = 0.01,\nshow_progress: bool = True\n) -> torch.Tensor:\n\"\"\"\nMulti-scale Deep Dream for better quality results.\n\nArgs:\nmodel: The DeepDream model wrapper\nimage: Input image tensor\nlayer_name: Which layer to dream on\nnum_octaves: Number of scales to process\noctave_scale: Scale factor between octaves\niterations_per_octave: Optimization steps per scale\nlearning_rate: Step size\nshow_progress: Whether to print progress\n\nReturns:\nDreamed image tensor\n\"\"\"\noriginal = image.clone()",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_64",
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  },
  {
    "text": "Returns:\nDreamed image tensor\n\"\"\"\noriginal = image.clone()\n\n# Calculate sizes for each octave (from smallest to largest)\n_, _, h, w = image.shape\noctave_sizes = []\nfor i in range(num_octaves - 1, -1, -1):\nscale = octave_scale ** i\noctave_sizes.append((int(h / scale), int(w / scale)))\n\n# Start with zeros (detail will be added)\ndetail = torch.zeros_like(image)\n\nfor octave_idx, (oh, ow) in enumerate(octave_sizes):\nif show_progress:\nprint(f\"\\nOctave {octave_idx + 1}/{num_octaves} - Size: {ow}x{oh}\")\n\n# Resize image and detail to current octave size\nscaled_image = torch.nn.functional.interpolate(\noriginal, size=(oh, ow), mode='bilinear', align_corners=False\n)\nscaled_detail = torch.nn.functional.interpolate(\ndetail, size=(oh, ow), mode='bilinear', align_corners=False\n)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_65",
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  {
    "text": "# Add detail from previous octave\ndream_input = scaled_image + scaled_detail\n\n# Dream at this scale\ndreamed = deep_dream(\nmodel,\ndream_input,\nlayer_name,\niterations=iterations_per_octave,\nlearning_rate=learning_rate,\nshow_progress=False\n)\n\n# Extract the detail (difference between dreamed and input)\ndetail = dreamed - scaled_image\n\n# Upscale detail for next octave\nif octave_idx < len(octave_sizes) - 1:\nnext_h, next_w = octave_sizes[octave_idx + 1]\ndetail = torch.nn.functional.interpolate(\ndetail, size=(next_h, next_w), mode='bilinear', align_corners=False\n)\n\n# Final result: original + accumulated detail\nfinal = original + torch.nn.functional.interpolate(\ndetail, size=(h, w), mode='bilinear', align_corners=False\n)\n\nreturn final\n\nprint(\"Multi-scale Deep Dream defined!\")",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_66",
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    "text": "return final\n\nprint(\"Multi-scale Deep Dream defined!\")\n\nMulti-scale Deep Dream defined!\n\nFull Flow Visualization¶\n\nOctave 1 (186x186):\nscaled_image ──────────────────┐\n├──→ dream_input ──→ [DREAM] ──→ dreamed\ndetail (zeros) ────────────────┘ │\n│\ndetail = dreamed - scaled_image ←──────┘\n│\n[UPSCALE to 261x261]\n│\n▼\nOctave 2 (261x261):\nscaled_image ──────────────────┐\n├──→ dream_input ──→ [DREAM] ──→ dreamed\ndetail (from octave 1) ────────┘ │\n│\ndetail = dreamed - scaled_image ←──────┘\n│\n[UPSCALE to 365x365]\n│\n▼\n... continue ...\n│\n▼\nFinal: original + detail (upscaled to 512x512)\n\nIn [12]:\n\n# Try the multi-scale version\noctave_dream = deep_dream_octaves(\nmodel,\noriginal_image,\nlayer_name='Mixed_6c',\nnum_octaves=4,\noctave_scale=1.4,\niterations_per_octave=30,\nlearning_rate=0.01\n)",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_67",
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    "text": "show_image(octave_dream, \"Multi-Scale Deep Dream (Mixed_6c)\")\n\nOctave 1/4 - Size: 112x145\n\nOctave 2/4 - Size: 158x204\n\nOctave 3/4 - Size: 221x285\n\nOctave 4/4 - Size: 310x400\n\n8. Compare Different Layers¶\n\nLet's visualize what different layers \"see\" by dreaming on multiple layers.\n\nIn [13]:\n\ndef compare_layers(model, image, layers, **kwargs):\n\"\"\"Dream on multiple layers and display results side by side.\"\"\"\nn_layers = len(layers)\nfig, axes = plt.subplots(1, n_layers + 1, figsize=(5 * (n_layers + 1), 5))\n\n# Show original\naxes[0].imshow(tensor_to_image(image))\naxes[0].set_title(\"Original\")\naxes[0].axis('off')",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_68",
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  {
    "text": "# Show original\naxes[0].imshow(tensor_to_image(image))\naxes[0].set_title(\"Original\")\naxes[0].axis('off')\n\n# Dream on each layer\nfor idx, layer in enumerate(layers):\nprint(f\"\\nProcessing layer: {layer}\")\ndreamed = deep_dream_octaves(model, image, layer, show_progress=False, **kwargs)\naxes[idx + 1].imshow(tensor_to_image(dreamed))\naxes[idx + 1].set_title(f\"{layer}\")\naxes[idx + 1].axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Compare early vs deep layers\ncompare_layers(\nmodel,\noriginal_image,\n['Mixed_5b', 'Mixed_6b', 'Mixed_7b'],\nnum_octaves=3,\niterations_per_octave=30\n)\n\nProcessing layer: Mixed_5b\n\nProcessing layer: Mixed_6b\n\nProcessing layer: Mixed_7b\n\n9. Why Do We See Dogs and Eyes?¶",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_69",
    "embedding": [
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    "text": "Processing layer: Mixed_5b\n\nProcessing layer: Mixed_6b\n\nProcessing layer: Mixed_7b\n\n9. Why Do We See Dogs and Eyes?¶\n\nYou might notice that Deep Dream often produces animal-like features, eyes, and fur patterns. This isn't random—it's because:\n\n• InceptionV3 was trained on ImageNet, which has over 100 dog breeds (out of 1000 total classes)\n•\n\n• The network has learned to be very good at detecting animal features\n•\n\n• When we amplify activations, we amplify whatever patterns the network knows best\n•\n\nThis is actually a profound insight into neural networks: they have biases based on their training data, and those biases become visible through techniques like Deep Dream.\n\nConnection to Modern AI Interpretability¶",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_70",
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    "text": "Connection to Modern AI Interpretability¶\n\nDeep Dream was one of the first \"feature visualization\" techniques. Today, researchers use similar ideas to:\n\n• Understand what models have learned (interpretability)\n•\n\n• Find model biases (fairness)\n•\n\n• Create adversarial examples (security)\n•\n\n• Generate art (creative AI)\n•\n\nThe core principle—using gradients to modify inputs—remains fundamental.\n\n10. Try Your Own Images!¶\n\nHere's a convenient function to dream on any image:\n\nIn [14]:\n\ndef dream_on_image(\nimage_path_or_url: str,\nlayer: str = 'Mixed_6c',\nmax_size: int = 512,\nnum_octaves: int = 4,\niterations: int = 10,\nlearning_rate: float = 0.01\n):\n\"\"\"\nConvenient function to apply Deep Dream to any image.",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_71",
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  {
    "text": "Args:\nimage_path_or_url: Path to local image or URL\nlayer: Which layer to dream on (see model.get_available_layers())\nmax_size: Maximum image dimension\nnum_octaves: Number of scales (more = slower but better)\niterations: Steps per octave\nlearning_rate: How strong the effect is\n\"\"\"\n# Load image\nimage = load_image(image_path_or_url, max_size=max_size)\n\n# Show original\nprint(\"Original:\")\nshow_image(image, \"Original\")\n\n# Dream\ndreamed = deep_dream_octaves(\nmodel, image, layer,\nnum_octaves=num_octaves,\niterations_per_octave=iterations,\nlearning_rate=learning_rate\n)\n\n# Show result\nprint(f\"\\nDreamed ({layer}):\")\nshow_image(dreamed, f\"Deep Dream - {layer}\")\n\nreturn dreamed",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_72",
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  {
    "text": "# Show result\nprint(f\"\\nDreamed ({layer}):\")\nshow_image(dreamed, f\"Deep Dream - {layer}\")\n\nreturn dreamed\n\n# Example usage:\n# result = dream_on_image(\"your_image.jpg\", layer='Mixed_6c')\n#\n# Or with a URL:\n# result = dream_on_image(\"https://example.com/image.jpg\")\n\nIn [15]:\n\nmodel.get_available_layers()\n\nOut[15]:\n\n['Conv2d_1a',\n'Conv2d_2b',\n'Mixed_5b',\n'Mixed_5c',\n'Mixed_5d',\n'Mixed_6a',\n'Mixed_6b',\n'Mixed_6c',\n'Mixed_6d',\n'Mixed_6e',\n'Mixed_7a',\n'Mixed_7b',\n'Mixed_7c']\n\nIn [16]:\n\n# Try it with another sample image\nnature_url = \"https://images.pexels.com/photos/414612/pexels-photo-414612.jpeg\"\n\n# Mountain landscape\nnature_url = \"https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=600\"\n\n# Forest\nnature_url = \"https://images.unsplash.com/photo-1448375240586-882707db888b?w=600\"",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_73",
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  {
    "text": "# Forest\nnature_url = \"https://images.unsplash.com/photo-1448375240586-882707db888b?w=600\"\n\n# Architecture (buildings create interesting geometric patterns)\n# nature_url = \"https://images.unsplash.com/photo-1486325212027-8081e485255e?w=600\"\n\n# Ocean waves\n# nature_url = \"https://images.unsplash.com/photo-1505118380757-91f5f5632de0?w=600\"\n\n# Abstract texture (good for showing pure pattern amplification)\n# nature_url = \"https://images.unsplash.com/photo-1558591710-4b4a1ae0f04d?w=600\"\n\nresult = dream_on_image(nature_url, layer='Mixed_6e', num_octaves=4, iterations=30)\n\nOriginal:\n\nOctave 1/4 - Size: 186x124\n\nOctave 2/4 - Size: 261x173\n\nOctave 3/4 - Size: 365x243\n\nOctave 4/4 - Size: 512x341\n\nDreamed (Mixed_6e):\n\nIn [17]:",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_74",
    "embedding": [
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  {
    "text": "Octave 1/4 - Size: 186x124\n\nOctave 2/4 - Size: 261x173\n\nOctave 3/4 - Size: 365x243\n\nOctave 4/4 - Size: 512x341\n\nDreamed (Mixed_6e):\n\nIn [17]:\n\nresult = dream_on_image(nature_url, layer='Mixed_7c', num_octaves=4, iterations=30)\n\nOriginal:\n\nOctave 1/4 - Size: 186x124\n\nOctave 2/4 - Size: 261x173\n\nOctave 3/4 - Size: 365x243\n\nOctave 4/4 - Size: 512x341\n\nDreamed (Mixed_7c):\n\n11. Save Your Creations¶\n\nIn [18]:\n\ndef save_dream(tensor: torch.Tensor, filename: str):\n\"\"\"Save a dreamed image to file.\"\"\"\nimg = tensor_to_image(tensor)\nimg_pil = Image.fromarray((img * 255).astype(np.uint8))\nimg_pil.save(filename)\nprint(f\"Saved to {filename}\")\n\n# Example:\n# save_dream(result, \"my_dream.png\")\n\nSummary¶\n\nWe've built Deep Dream from scratch! Here's what we learned:",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_75",
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  {
    "text": "# Example:\n# save_dream(result, \"my_dream.png\")\n\nSummary¶\n\nWe've built Deep Dream from scratch! Here's what we learned:\n\n• Neural networks detect hierarchical features - edges → textures → parts → objects\n•\n\n• Gradient ascent on inputs - instead of training the network, we modify the image\n•\n\n• Multi-scale processing - improves visual quality\n•\n\n• Networks have biases - they \"see\" what they were trained to recognize\n•\n\nKey Takeaways for Understanding AI:¶\n\n• Feature visualization reveals what networks learn\n•\n\n• Training data biases become visible in network behavior\n•\n\n• The same gradient-based principles underpin adversarial examples, style transfer, and more\n•\n\nNext Steps:¶\n\n• Try different pre-trained models (VGG, ResNet)\n•",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_76",
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  {
    "text": "Next Steps:¶\n\n• Try different pre-trained models (VGG, ResNet)\n•\n\n• Experiment with maximizing specific neurons instead of whole layers\n•\n\n• Combine with style transfer for even more creative results\n•\n\n• Explore modern interpretability tools like Lucid or TransformerLens\n•\n\nThanks for reading! If you found this helpful, consider following for more deep learning tutorials.\n\nIn [19]:\n\n!jupyter nbconvert --to html https://colab.research.google.com/drive/1EvQjVea0M3qkUVMkil3ZtujnGcRGjBB_?usp=drive_link\n\n[NbConvertApp] WARNING | pattern '/content/deep_dream_tutorial.ipynb' matched no files\nThis application is used to convert notebook files (*.ipynb)\nto various other formats.\n\nWARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.",
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    "text": "WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.\n\nOptions\n=======\nThe options below are convenience aliases to configurable class-options,\nas listed in the \"Equivalent to\" description-line of the aliases.\nTo see all configurable class-options for some <cmd>, use:\n<cmd> --help-all",
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  {
    "text": "--debug\nset log level to logging.DEBUG (maximize logging output)\nEquivalent to: [--Application.log_level=10]\n--show-config\nShow the application's configuration (human-readable format)\nEquivalent to: [--Application.show_config=True]\n--show-config-json\nShow the application's configuration (json format)\nEquivalent to: [--Application.show_config_json=True]\n--generate-config\ngenerate default config file\nEquivalent to: [--JupyterApp.generate_config=True]\n-y\nAnswer yes to any questions instead of prompting.\nEquivalent to: [--JupyterApp.answer_yes=True]\n--execute\nExecute the notebook prior to export.\nEquivalent to: [--ExecutePreprocessor.enabled=True]\n--allow-errors",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_79",
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    "text": "Disable chromium security sandbox when converting to PDF..\nEquivalent to: [--WebPDFExporter.disable_sandbox=True]\n--show-input\nShows code input. This flag is only useful for dejavu users.\nEquivalent to: [--TemplateExporter.exclude_input=False]\n--embed-images\nEmbed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.\nEquivalent to: [--HTMLExporter.embed_images=True]\n--sanitize-html\nWhether the HTML in Markdown cells and cell outputs should be sanitized..\nEquivalent to: [--HTMLExporter.sanitize_html=True]\n--log-level=<Enum>\nSet the log level by value or name.\nChoices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']\nDefault: 30\nEquivalent to: [--Application.log_level]\n--config=<Unicode>",
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    "chunk_id": "notebook: deep_dream_tutorial.html_83",
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  {
    "text": "Equivalent to: [--NbConvertApp.writer_class]\n--post=<DottedOrNone>\nPostProcessor class used to write the\nresults of the conversion\nDefault: ''\nEquivalent to: [--NbConvertApp.postprocessor_class]\n--output=<Unicode>\nOverwrite base name use for output files.\nSupports pattern replacements '{notebook_name}'.\nDefault: '{notebook_name}'\nEquivalent to: [--NbConvertApp.output_base]\n--output-dir=<Unicode>\nDirectory to write output(s) to. Defaults\nto output to the directory of each notebook. To recover\nprevious default behaviour (outputting to the current\nworking directory) use . as the flag value.\nDefault: ''\nEquivalent to: [--FilesWriter.build_directory]\n--reveal-prefix=<Unicode>\nThe URL prefix for reveal.js (version 3.x).\nThis defaults to the reveal CDN, but can be any url pointing to a copy",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_86",
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    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_87",
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    "text": "Examples\n--------\n\nThe simplest way to use nbconvert is\n\n> jupyter nbconvert mynotebook.ipynb --to html\n\nOptions include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf'].\n\n> jupyter nbconvert --to latex mynotebook.ipynb\n\nBoth HTML and LaTeX support multiple output templates. LaTeX includes\n'base', 'article' and 'report'. HTML includes 'basic', 'lab' and\n'classic'. You can specify the flavor of the format used.\n\n> jupyter nbconvert --to html --template lab mynotebook.ipynb\n\nYou can also pipe the output to stdout, rather than a file\n\n> jupyter nbconvert mynotebook.ipynb --stdout\n\nPDF is generated via latex\n\n> jupyter nbconvert mynotebook.ipynb --to pdf",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_88",
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  {
    "text": "> jupyter nbconvert mynotebook.ipynb --stdout\n\nPDF is generated via latex\n\n> jupyter nbconvert mynotebook.ipynb --to pdf\n\nYou can get (and serve) a Reveal.js-powered slideshow\n\n> jupyter nbconvert myslides.ipynb --to slides --post serve\n\nMultiple notebooks can be given at the command line in a couple of\ndifferent ways:\n\n> jupyter nbconvert notebook*.ipynb\n> jupyter nbconvert notebook1.ipynb notebook2.ipynb\n\nor you can specify the notebooks list in a config file, containing::\n\nc.NbConvertApp.notebooks = [\"my_notebook.ipynb\"]\n\n> jupyter nbconvert --config mycfg.py\n\nTo see all available configurables, use `--help-all`.\n\nIn [20]:\n\nfrom google.colab import drive\ndrive.mount('/content/drive')\n\nMounted at /content/drive",
    "source": "notebook: deep_dream_tutorial.html",
    "type": "html_notebook",
    "chunk_id": "notebook: deep_dream_tutorial.html_89",
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    "text": "Word_Vectors\n\nWord Vectors&#182;\n\nI've taken this great lecture \"Sequence Models\" from Coursera given by DeepLearning.AI. this notebook is dervied from the week2 homework.\n\nWe will use pre-trained set of embeddings, learn cosine similiarty, how to solve word analogy between two words. And the most exciting part is to modify the words to reduce their gender bias.\n\nTable of Contents&#182;\n\n• Packages\n•\n\n• 1 - Load the Word Vectors\n•\n\n• 2 - Cosine Similarity\n•\n\n• 3 - Word Analogy Task\n•\n\n• 4 - Debiasing Word Vectors\n\n• 4.1 - Neutralize Bias for Non-Gender Specific Words\n•\n\n• 4.2 - Equalization Algorithm for Gender-Specific Words\n•\n\n•\n\n• 5 - References\n•\n\nPackages&#182;\n\nLoad packages\n\nIn [1]:\n\nimport numpy as np\n\n1 - Load the Word Vectors&#182;",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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    "text": "•\n\n• 5 - References\n•\n\nPackages&#182;\n\nLoad packages\n\nIn [1]:\n\nimport numpy as np\n\n1 - Load the Word Vectors&#182;\n\nFor this assignment, you'll use 50-dimensional GloVe vectors to represent words.\nRun the following cell to load the word_to_vec_map.\n\nIn [2]:\n\ndef read_glove_vecs(glove_file):\nwith open(glove_file, 'r') as f:\nwords = set()\nword_to_vec_map = {}\n\nfor line in f:\nline = line.strip().split()\ncurr_word = line[0]\nwords.add(curr_word)\nword_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)\n\nreturn words, word_to_vec_map\n\nIn [3]:\n\nwords, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')\n\nIn [4]:\n\nprint(len(words))\n\n400000\n\nIn [5]:\n\nfor idx, (key, value) in enumerate(word_to_vec_map.items()):\nprint(\"word: \", key)\nprint(\"shape\", value.shape)\nif idx ==1:\nbreak",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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    "text": "400000\n\nIn [5]:\n\nfor idx, (key, value) in enumerate(word_to_vec_map.items()):\nprint(\"word: \", key)\nprint(\"shape\", value.shape)\nif idx ==1:\nbreak\n\nword: the\nshape (50,)\nword: ,\nshape (50,)\n\nAs you can see above, there are 400000 words in the corpus and the shape of each word vector is 50.\n\n2 - Cosine Similarity&#182;\n\nWe will use cosine similiarity to measure the similarity between two words. Given two vectors $u$ and $v$, cosine similarity is defined as follows:\n\n$$\\text{CosineSimilarity(u, v)} = \\frac {u \\cdot v} {||u||_2 ||v||_2} = cos(\\theta) \\tag{1}$$\nThe norm of $u$ is defined as $ ||u||_2 = \\sqrt{\\sum_{i=1}^{n} u_i^2}$\n\nNote that L2 (Euclidean) distance also works.\n\nLet's see an example before applying it within a function:\n\nIn [6]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_2",
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  {
    "text": "Note that L2 (Euclidean) distance also works.\n\nLet's see an example before applying it within a function:\n\nIn [6]:\n\n# dot product\nu_v = np.dot(word_to_vec_map[\"city\"], word_to_vec_map[\"town\"])\nprint(u_v)\n\n# norm of 2 words:\nnorm_u = np.sqrt(np.sum(np.square(word_to_vec_map[\"city\"])))\nnorm_v = np.sqrt(np.sum(np.square(word_to_vec_map[\"town\"])))\n\n# simple version of norm:\nnorm_u = np.sqrt(np.dot(word_to_vec_map[\"city\"], word_to_vec_map[\"city\"]))\nnorm_v = np.sqrt(np.dot(word_to_vec_map[\"town\"], word_to_vec_map[\"town\"]))\nprint(norm_u)\nprint(norm_v)\n\n# cosine similarity:\nu_v/(norm_u * norm_v)\n\n27.940339194829\n5.683819631019707\n5.658190970652017\n\nOut[6]:\n\n0.868787942931717\n\nIn [7]:\n\ndef cosine_similarity(u, v):\n\"\"\"\nCosine similarity reflects the degree of similarity between u and v",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_3",
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    "text": "Out[6]:\n\n0.868787942931717\n\nIn [7]:\n\ndef cosine_similarity(u, v):\n\"\"\"\nCosine similarity reflects the degree of similarity between u and v\n\nArguments:\nu -- a word vector of shape (n,)\nv -- a word vector of shape (n,)\n\nReturns:\ncosine_similarity -- the cosine similarity between u and v defined by the formula above.\n\"\"\"\n\n# Special case. Consider the case u = [0, 0], v=[0, 0]\nif np.all(u == v):\nreturn 1\n\ndot = np.dot(u,v)\nnorm_u = np.sqrt(np.sum(np.square(u)))\nnorm_v = np.sqrt(np.sum(np.square(v)))\n# avoid division by zero error\nif np.isclose(norm_u * norm_v, 0, atol=1e-32):\nreturn 0\n# Compute the cosine similarity defined by formula (1) (≈1 line)\ncosine_similarity = dot / (norm_u * norm_v)\n\nreturn cosine_similarity\n\nBelow you may find examples of words and their cosine similarity:\n\nIn [8]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_4",
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    "text": "return cosine_similarity\n\nBelow you may find examples of words and their cosine similarity:\n\nIn [8]:\n\nfather = word_to_vec_map[\"father\"]\nmother = word_to_vec_map[\"mother\"]\nball = word_to_vec_map[\"ball\"]\ncrocodile = word_to_vec_map[\"crocodile\"]\nfrance = word_to_vec_map[\"france\"]\nitaly = word_to_vec_map[\"italy\"]\nparis = word_to_vec_map[\"paris\"]\nrome = word_to_vec_map[\"rome\"]\n\nprint(\"cosine_similarity(father, mother) = \", cosine_similarity(father, mother))\nprint(\"cosine_similarity(ball, crocodile) = \",cosine_similarity(ball, crocodile))\nprint(\"cosine_similarity(france - paris, rome - italy) = \",cosine_similarity(france - paris, rome - italy))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_5",
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  {
    "text": "cosine_similarity(father, mother) = 0.8909038442893615\ncosine_similarity(ball, crocodile) = 0.2743924626137942\ncosine_similarity(france - paris, rome - italy) = -0.6751479308174201\n\nThe cosine similarity from man - actor, woman - actress is around ~90% while the opposite is around ~50%. It makes sense.\n\nIn [9]:\n\nprint(cosine_similarity(word_to_vec_map[\"man\"] - word_to_vec_map[\"actor\"],\nword_to_vec_map[\"woman\"]-word_to_vec_map[\"actress\"]))\nprint(cosine_similarity(word_to_vec_map[\"man\"] - word_to_vec_map[\"actress\"],\nword_to_vec_map[\"woman\"]-word_to_vec_map[\"actor\"]))\n\n0.9146492195406316\n0.5060072094699283\n\nLet's check another job:\n\nIn [10]:\n\nprint(cosine_similarity(word_to_vec_map[\"man\"] - word_to_vec_map[\"engineer\"],\nword_to_vec_map[\"woman\"]-word_to_vec_map[\"nurse\"]))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_6",
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    "text": "In [10]:\n\nprint(cosine_similarity(word_to_vec_map[\"man\"] - word_to_vec_map[\"engineer\"],\nword_to_vec_map[\"woman\"]-word_to_vec_map[\"nurse\"]))\n\nprint(cosine_similarity(word_to_vec_map[\"man\"] - word_to_vec_map[\"nurse\"],\nword_to_vec_map[\"woman\"]-word_to_vec_map[\"engineer\"]))\n\n0.5709592438104082\n0.23166955887222856\n\nUnfortunately man to engineer and woman to nurse have a higher similarity than man to nurse woman to engineer.\n\nSimple Example for Gender Biased Words&#182;\n\nI will create woman and man word embeddings. Here the first element repsresents the gender. -1 for man, 1 for woman.\n\nIn [11]:\n\nman = np.array([-1,0.1,0.6])\nwoman = np.array([1,0.5,0.3])",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_7",
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    "text": "In [11]:\n\nman = np.array([-1,0.1,0.6])\nwoman = np.array([1,0.5,0.3])\n\nAnd I would like to create 2 words (and I aim to them be gender biased.) So that, the first element of lipstick is positive and the first element of war is negative.\n\nIn [12]:\n\nlipstick = np.array([0.1, -0.1, 0])\nwar = np.array([-0.1, 0.1, 0])\n\nHow can we understand that they are gender biased?\n\nFirst, we will take a difference between man and woman. The only difference between man and woman should gender. We will see how other features will be equalized later.\n\nIn [13]:\n\ndiff = woman - man\nprint(\"diff\", diff)\n\ndiff [ 2. 0.4 -0.3]\n\nThen, we will check the similarty between this difference and given word. If the similarty is positive it means that the word is related to woman, otherwise it will be related to man.",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "In [14]:\n\ndef get_similarity(diff, word):\nprint(\"np.dot:\", np.dot(diff, word))\nprint(\"L2 diff\", np.sqrt(np.sum(np.square(diff))))\nprint(\"L2 word: \", np.sqrt(np.sum(np.square(word))))\ncos_sim = np.dot(diff, word) / (np.sqrt(np.sum(np.square(diff))) * np.sqrt(np.sum(np.square(word))))\nprint(\"cos sim\", cos_sim)\n\nThe cosine similarity between the difference and \"war\" is negative:\n\nIn [15]:\n\nget_similarity(diff, war)\n\nnp.dot: -0.16\nL2 diff 2.0615528128088303\nL2 word: 0.14142135623730953\ncos sim -0.5487954724560281\n\nThe cosine similarity between the difference and \"lipstick\" is positive:\n\nIn [16]:\n\nget_similarity(diff, lipstick)\n\nnp.dot: 0.16\nL2 diff 2.0615528128088303\nL2 word: 0.14142135623730953\ncos sim 0.5487954724560281\n\nWe will explore how to reduce gender bias from these words later.",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_9",
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    "text": "We will explore how to reduce gender bias from these words later.\n\n3 - Word Analogy Task&#182;\n\n•\nIn the word analogy task, complete this sentence:\n\n\"a is to b as c is to ____\".\n\n•\n\n•\nAn example is:\n\n'man is to woman as king is to queen' .\n\n•\n\nIn [17]:\n\ndef complete_analogy(word_a, word_b, word_c, word_to_vec_map):\n\"\"\"\nPerforms the word analogy task as explained above: a is to b as c is to ____.\n\nArguments:\nword_a -- a word, string\nword_b -- a word, string\nword_c -- a word, string\nword_to_vec_map -- dictionary that maps words to their corresponding vectors.\n\nReturns:\nbest_word -- the word such that v_b - v_a is close to v_best_word - v_c, as measured by cosine similarity\n\"\"\"\n\n# convert words to lowercase\nword_a, word_b, word_c = word_a.lower(), word_b.lower(), word_c.lower()",
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    "text": "# convert words to lowercase\nword_a, word_b, word_c = word_a.lower(), word_b.lower(), word_c.lower()\n\n# Get the word embeddings e_a, e_b and e_c\ne_a, e_b, e_c = word_to_vec_map[word_a], word_to_vec_map[word_b], word_to_vec_map[word_c]\n\nwords = word_to_vec_map.keys()\n#initialize max_cosine_sim as large neg number\nmax_cosine_sim = -100\nbest_word = None\n\n# loop over the whole word vector set\nfor w in words:\n# skip word_c from query\nif w == word_c:\ncontinue\n\ncosine_sim = cosine_similarity(e_b - e_a, word_to_vec_map[w.lower()] - e_c)\nif cosine_sim > max_cosine_sim:\nmax_cosine_sim = cosine_sim\nbest_word = w\n\nreturn best_word\n\nIn [18]:",
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  {
    "text": "return best_word\n\nIn [18]:\n\nexample_list = [('italy', 'italian', 'spain'),\n('india', 'delhi', 'japan'),\n('man', 'woman', 'boy'),\n('small', 'smaller', 'large'),\n('small', 'smaller', 'big')]\nfor triad in example_list:\nprint ('{} -> {} :: {} -> {}'.format( *triad, complete_analogy(*triad, word_to_vec_map)))\n\nitaly -> italian :: spain -> spanish\nindia -> delhi :: japan -> tokyo\nman -> woman :: boy -> girl\nsmall -> smaller :: large -> smaller\nsmall -> smaller :: big -> competitors\n\nAs you can see, it can't catch \"bigger\" for the latest example.\n\n4 - Debiasing Word Vectors&#182;\n\nLet's begin by computing a vector $g = e_{woman}-e_{man}$.\n\nThe resulting vector $g$ roughly encodes the concept of \"gender\".",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_12",
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    "text": "Let's begin by computing a vector $g = e_{woman}-e_{man}$.\n\nThe resulting vector $g$ roughly encodes the concept of \"gender\".\n\nThis is not the best way of reducing the gender bias. It doesn't eliminate all traces of bias, the bias direction $g$ was defined using only the pair of words woman and man.\n\nif $g$ were defined by computing $g_1 = e_{woman} - e_{man}$; $g_2 = e_{mother} - e_{father}$; $g_3 = e_{girl} - e_{boy}$; and so on and averaging over them, you would obtain a better estimate of the \"gender\" dimension in the 50 dimensional word embedding space.\n\nIn [19]:\n\ng = word_to_vec_map['woman'] - word_to_vec_map['man']\nprint(g)",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_13",
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    "text": "In [19]:\n\ng = word_to_vec_map['woman'] - word_to_vec_map['man']\nprint(g)\n\n[-0.087144 0.2182 -0.40986 -0.03922 -0.1032 0.94165\n-0.06042 0.32988 0.46144 -0.35962 0.31102 -0.86824\n0.96006 0.01073 0.24337 0.08193 -1.02722 -0.21122\n0.695044 -0.00222 0.29106 0.5053 -0.099454 0.40445\n0.30181 0.1355 -0.0606 -0.07131 -0.19245 -0.06115\n-0.3204 0.07165 -0.13337 -0.25068714 -0.14293 -0.224957\n-0.149 0.048882 0.12191 -0.27362 -0.165476 -0.20426\n0.54376 -0.271425 -0.10245 -0.32108 0.2516 -0.33455\n-0.04371 0.01258 ]\n\nIn [20]:\n\nprint ('List of names and their similarities with constructed vector:')\n\n# girls and boys name\nname_list = ['john', 'marie', 'sophie', 'ronaldo', 'priya', 'rahul', 'danielle', 'reza',\n'katy', 'yasmin']\n\nfor w in name_list:\nprint (w, cosine_similarity(word_to_vec_map[w], g))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_14",
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  {
    "text": "for w in name_list:\nprint (w, cosine_similarity(word_to_vec_map[w], g))\n\nList of names and their similarities with constructed vector:\njohn -0.23163356145973724\nmarie 0.315597935396073\nsophie 0.31868789859418784\nronaldo -0.31244796850329437\npriya 0.17632041839009402\nrahul -0.16915471039231716\ndanielle 0.24393299216283895\nreza -0.07930429672199553\nkaty 0.2831068659572615\nyasmin 0.23313857767928758\n\nAs you can see, female first names tend to have a positive cosine similarity with our constructed vector $g$, while male first names tend to have a negative cosine similarity. This is not surprising, and the result seems acceptable.\n\nNow try with some other words:\n\nIn [21]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "Now try with some other words:\n\nIn [21]:\n\nprint('Other words and their similarities:')\nword_list = ['lipstick', 'guns', 'science', 'arts', 'literature', 'warrior','doctor', 'tree', 'receptionist',\n'technology', 'fashion', 'teacher', 'engineer', 'pilot', 'computer', 'singer']\nfor w in word_list:\nprint (w, cosine_similarity(word_to_vec_map[w], g))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_16",
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    "text": "Other words and their similarities:\nlipstick 0.2769191625638267\nguns -0.1888485567898898\nscience -0.06082906540929701\narts 0.008189312385880337\nliterature 0.06472504433459932\nwarrior -0.20920164641125288\ndoctor 0.11895289410935041\ntree -0.07089399175478091\nreceptionist 0.33077941750593737\ntechnology -0.13193732447554302\nfashion 0.03563894625772699\nteacher 0.17920923431825664\nengineer -0.0803928049452407\npilot 0.0010764498991916937\ncomputer -0.10330358873850498\nsinger 0.1850051813649629\n\n“computer” is negative and is closer in value to male first names, while “literature” is positive and is closer to female first names\n\nHere you may find the cosine similarities between man, woman and given word:\n\nIn [22]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_17",
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  {
    "text": "Here you may find the cosine similarities between man, woman and given word:\n\nIn [22]:\n\nprint('Other words and their similarities:')\nword_list_1 = ['lipstick', 'arts', 'literature','doctor', 'tree', 'receptionist',\n'fashion', 'teacher', 'singer']\nword_list_2 = [ 'guns', 'science', 'warrior',\n'technology', 'engineer', 'pilot', 'computer']\nfor w in word_list_1:\nprint (\"woman\", w, cosine_similarity(word_to_vec_map[w], word_to_vec_map[\"woman\"]))\nprint (\"man\", w, cosine_similarity(word_to_vec_map[w], word_to_vec_map[\"man\"]))\n\nfor w in word_list_2:\nprint (\"woman\", w, cosine_similarity(word_to_vec_map[w], word_to_vec_map[\"woman\"]))\nprint (\"man\", w, cosine_similarity(word_to_vec_map[w], word_to_vec_map[\"man\"]))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_18",
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  {
    "text": "Other words and their similarities:\nwoman lipstick 0.3862429669706063\nman lipstick 0.273165272354283\nwoman arts 0.2940187431766971\nman arts 0.3084874532181791\nwoman literature 0.363119683771546\nman literature 0.35388764809819956\nwoman doctor 0.7252735302545701\nman doctor 0.7119579038910591\nwoman tree 0.37484879731523546\nman tree 0.43365820275424793\nwoman receptionist 0.4464364858934247\nman receptionist 0.3104243175079627\nwoman fashion 0.4624789392055569\nman fashion 0.4739439919224705\nwoman teacher 0.68499316078363\nman teacher 0.6392358303810439\nwoman singer 0.5692962568351047\nman singer 0.5133699757062065\nwoman guns 0.3138791334333115\nman guns 0.4273814491100573\nwoman science 0.31239626692008643\nman science 0.3622743278942722\nwoman warrior 0.4713955678516831",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_19",
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    "text": "4.1 - Neutralize Bias for Non-Gender Specific Words&#182;\n\nBoliukbasi et al., 2016\n\nThe figure below should help you visualize what neutralizing does. If you're using a 50-dimensional word embedding, the 50 dimensional space can be split into two parts: The bias-direction $g$, and the remaining 49 dimensions, which is called $g_{\\perp}$ here. In linear algebra, we say that the 49-dimensional $g_{\\perp}$ is perpendicular (or \"orthogonal\") to $g$, meaning it is at 90 degrees to $g$. The neutralization step takes a vector such as $e_{receptionist}$ and zeros out the component in the direction of $g$, giving us $e_{receptionist}^{debiased}$.",
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  {
    "text": "Even though $g_{\\perp}$ is 49-dimensional, given the limitations of what you can draw on a 2D screen, it's illustrated using a 1-dimensional axis below.\n\nFigure 2: The word vector for \"receptionist\" represented before and after applying the neutralize operation.\n\nExercise 3 - neutralize&#182;\n\nImplement neutralize() to remove the bias of words such as \"receptionist\" or \"scientist.\"\n\nGiven an input embedding $e$, you can use the following formulas to compute $e^{debiased}$:\n\n$$e^{bias\\_component} = \\frac{e \\cdot g}{||g||_2^2} * g\\tag{2}$$$$e^{debiased} = e - e^{bias\\_component}\\tag{3}$$\nIf you are an expert in linear algebra, you may recognize $e^{bias\\_component}$ as the projection of $e$ onto the direction $g$. If you're not an expert in linear algebra, don't worry about this. ;)",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_22",
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    "text": "In [23]:\n\ndef neutralize(word, g, word_to_vec_map):\n\"\"\"\nRemoves the bias of \"word\" by projecting it on the space orthogonal to the bias axis.\nThis function ensures that gender neutral words are zero in the gender subspace.\n\nArguments:\nword -- string indicating the word to debias\ng -- numpy-array of shape (50,), corresponding to the bias axis (such as gender)\nword_to_vec_map -- dictionary mapping words to their corresponding vectors.\n\nReturns:\ne_debiased -- neutralized word vector representation of the input \"word\"\n\"\"\"\n\ne = word_to_vec_map[word]\ne_biascomponent = np.dot(e, g) / np.dot(g, g) * g\ne_debiased = e - e_biascomponent\n\nreturn e_debiased\n\nIn [24]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "e = word_to_vec_map[word]\ne_biascomponent = np.dot(e, g) / np.dot(g, g) * g\ne_debiased = e - e_biascomponent\n\nreturn e_debiased\n\nIn [24]:\n\ne = \"receptionist\"\nprint(\"cosine similarity between \" + e + \" and g, before neutralizing: \", cosine_similarity(word_to_vec_map[\"receptionist\"], g))\n\ne_debiased = neutralize(\"receptionist\", g, word_to_vec_map)\nprint(\"cosine similarity between \" + e + \" and g, after neutralizing: \", cosine_similarity(e_debiased, g))\n\ncosine similarity between receptionist and g, before neutralizing: 0.33077941750593737\ncosine similarity between receptionist and g, after neutralizing: -2.6832242276243644e-17\n\n4.2 - Equalization Algorithm for Gender-Specific Words&#182;",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_24",
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  {
    "text": "4.2 - Equalization Algorithm for Gender-Specific Words&#182;\n\nEqualization is applied to pairs of words that you might want to have differ only through the gender property. As a concrete example, suppose that \"actress\" is closer to \"babysit\" than \"actor.\" By applying neutralization to \"babysit,\" you can reduce the gender stereotype associated with babysitting. But this still does not guarantee that \"actor\" and \"actress\" are equidistant from \"babysit.\" The equalization algorithm takes care of this.",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_25",
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    "text": "The key idea behind equalization is to make sure that a particular pair of words are equidistant from the 49-dimensional $g_\\perp$. The equalization step also ensures that the two equalized steps are now the same distance from $e_{receptionist}^{debiased}$, or from any other work that has been neutralized. Visually, this is how equalization works:\n\nThe derivation of the linear algebra to do this is a bit more complex. (See Bolukbasi et al., 2016 in the References for details.) Here are the key equations:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_26",
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    "text": "$$ \\mu = \\frac{e_{w1} + e_{w2}}{2}\\tag{4}$$$$ \\mu_{B} = \\frac {\\mu \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n\\tag{5}$$$$\\mu_{\\perp} = \\mu - \\mu_{B} \\tag{6}$$$$ e_{w1B} = \\frac {e_{w1} \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n\\tag{7}$$\n$$ e_{w2B} = \\frac {e_{w2} \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n\\tag{8}$$\n\n$$e_{w1B}^{corrected} = \\sqrt{ |{1 - ||\\mu_{\\perp} ||^2_2} |} * \\frac{e_{\\text{w1B}} - \\mu_B} {||(e_{w1} - \\mu_{\\perp}) - \\mu_B||_2} \\tag{9}$$$$e_{w2B}^{corrected} = \\sqrt{ |{1 - ||\\mu_{\\perp} ||^2_2} |} * \\frac{e_{\\text{w2B}} - \\mu_B} {||(e_{w2} - \\mu_{\\perp}) - \\mu_B||_2} \\tag{10}$$$$e_1 = e_{w1B}^{corrected} + \\mu_{\\perp} \\tag{11}$$$$e_2 = e_{w2B}^{corrected} + \\mu_{\\perp} \\tag{12}$$\n\nIn [25]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_27",
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    "text": "In [25]:\n\ndef equalize(pair, bias_axis, word_to_vec_map):\n\"\"\"\nDebias gender specific words by following the equalize method described in the figure above.\n\nArguments:\npair -- pair of strings of gender specific words to debias, e.g. (\"actress\", \"actor\")\nbias_axis -- numpy-array of shape (50,), vector corresponding to the bias axis, e.g. gender\nword_to_vec_map -- dictionary mapping words to their corresponding vectors\n\nReturns\ne_1 -- word vector corresponding to the first word\ne_2 -- word vector corresponding to the second word\n\"\"\"\n\nw1, w2 = pair[0], pair[1]\ne_w1, e_w2 = word_to_vec_map[w1], word_to_vec_map[w2]\n\n# Step 2: Compute the mean of e_w1 and e_w2\nmu = (e_w1 + e_w2)/2\n\n# Step 3: Compute the projections of mu over the bias axis and the orthogonal axis",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "# Step 2: Compute the mean of e_w1 and e_w2\nmu = (e_w1 + e_w2)/2\n\n# Step 3: Compute the projections of mu over the bias axis and the orthogonal axis\n\nmu_B = np.dot(mu, bias_axis) / np.dot(bias_axis, bias_axis) * bias_axis\nmu_orth = mu - mu_B\n\n# Step 4: Use equations (7) and (8) to compute e_w1B and e_w2B\ne_w1B = np.dot(e_w1, bias_axis) / np.dot(bias_axis, bias_axis) * bias_axis\ne_w2B = np.dot(e_w2, bias_axis) / np.dot(bias_axis, bias_axis) * bias_axis\n\n# Step 5: Adjust the Bias part of e_w1B and e_w2B using the formulas (9) and (10) given above\ncorrected_e_w1B = np.sqrt(abs(1- np.dot(mu_orth, mu_orth))) * (e_w1B - mu_B) / abs((e_w1 - mu_orth) - mu_B)\ncorrected_e_w2B = np.sqrt(abs(1- np.dot(mu_orth, mu_orth))) * (e_w2B - mu_B) / abs((e_w2 - mu_orth) - mu_B)",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2023-01-01-Word_Vectors.html_29",
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  {
    "text": "# Step 6: Debias by equalizing e1 and e2 to the sum of their corrected projections\ne1 = corrected_e_w1B + mu_orth\ne2 = corrected_e_w2B + mu_orth\n\nreturn e1, e2\n\nIn [26]:\n\nprint(\"cosine similarities before equalizing:\")\nprint(\"cosine_similarity(word_to_vec_map[\\\"man\\\"], gender) = \", cosine_similarity(word_to_vec_map[\"man\"], g))\nprint(\"cosine_similarity(word_to_vec_map[\\\"woman\\\"], gender) = \", cosine_similarity(word_to_vec_map[\"woman\"], g))\nprint()\ne1, e2 = equalize((\"man\", \"woman\"), g, word_to_vec_map)\nprint(\"cosine similarities after equalizing:\")\nprint(\"cosine_similarity(e1, gender) = \", cosine_similarity(e1, g))\nprint(\"cosine_similarity(e2, gender) = \", cosine_similarity(e2, g))",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "cosine similarities before equalizing:\ncosine_similarity(word_to_vec_map[&#34;man&#34;], gender) = -0.11711095765336832\ncosine_similarity(word_to_vec_map[&#34;woman&#34;], gender) = 0.35666618846270376\n\ncosine similarities after equalizing:\ncosine_similarity(e1, gender) = -0.7165727525843935\ncosine_similarity(e2, gender) = 0.7396596474928908\n\n5 - References&#182;\n\n• The debiasing algorithm is from Bolukbasi et al., 2016, [Man is to Computer Programmer as Woman is to\n•\n\nHomemaker? Debiasing Word Embeddings](https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf)\n\n• The GloVe word embeddings were due to Jeffrey Pennington, Richard Socher, and Christopher D. Manning. (https://nlp.stanford.edu/projects/glove/)\n•\n\nIn [ ]:",
    "source": "notebook: 2023-01-01-Word_Vectors.html",
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  {
    "text": "Anomaly_Detection_with_Numpy\n\nAnomaly Detection&#182;\n\nThese are my notes & codes from the lecture: Unsupervised Learning, Recommenders, Reinforcement Learning given by DeepLearning.AI in Coursera. In this notebook I will explain the anomaly detection with numpy. You may find the original notebook here.\n\nData includes: X_train, X_val and y_val.\n\n• Find mean, variance from training set.\n•\n\n• Calculate the p values from training set.\n•\n\n• Seelct the threshold epsilon using validation set:\n\n• Find p_val values from validation set.\n•\n\n• Get the range of p_val and create list of potential epsilon values.\n•\n\n• Try each of the potential epsilon values to calculate the TP, FP and TN. Calculate F1 score.\n•\n\n• Find the threshold which gives the maximum F1 score.\n•\n\n•",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_0",
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  {
    "text": "• Find the threshold which gives the maximum F1 score.\n•\n\n•\n\n• Find the anomalies in training set using the selected epsilon value.\n•\n\nLet's import the libraries.\n\nIn [1]:\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom utils import *\n\n%matplotlib inline\n\nWe will use the training dataset to fit the Gaussian distribution. Then we will use validation dataset and known true y values to select the treshold.\n\nDataset&#182;\n\nIn [2]:\n\nX_train = np.array([[3.8, 4.0], [5.6, 4.7], [6.3, 3], [2.9, 4.6], [3.2, 4.9], [8,6], [9,7], [6.5,7], [5,5], [4,3], [6,7],\n[7.4, 9.5], [4,5], [3,7], [5,6], [14, 7], [3.8,4.6], [4, 5.5], [3,7.8], [5,7], [4,5], [8.5,1], [4,1.5], [3,2], [8,1],\n[10,3], [6,6], [9,5], [3,1.2], [8.5,10], [7,4], [5,4], [3,7], [12,10.5], [7,8], [10,9.2]])",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_1",
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  {
    "text": "X_val = np.array([[3, 4.0], [5, 6], [6.1, 3.2], [1, 1.5], [4.6, 8], [7,9], [12,15], [4,7], [5,3], [3.5, 7], [14,2], [3, 9.5],\n[4,5.4], [3.2,3.4], [1,6.5], [13, 3.5], [12.5,6.4], [6.7, 12], [3.5,4.5], [4,9], [4,5], [8,1], [0.5,1.5],\n[3.1,5], [2,8.7], [9.1,3.4], [4,4], [8,12], [4,1], [11,10]])\n\ny_val = np.array([0,0,0,0,0,0,1,0,0,0,1,0,\n0,0,0,1,1,1,0,0,0,0,0,\n0,0,0,0,1,0,0])\n\nIn [3]:\n\nprint(X_train.shape)\nprint(X_val.shape)\nprint(y_val.shape)\n\n(36, 2)\n(30, 2)\n(30,)\n\nIn [4]:\n\n# Create a scatter plot of the data. To change the markers to blue \"x\",\n# we used the 'marker' and 'c' parameters\nplt.scatter(X_train[:, 0], X_train[:, 1], marker='x', c='b')",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
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  {
    "text": "# Set the title\nplt.title(\"The first dataset\")\n# Set the y-axis label\nplt.ylabel('Throughput (mb/s)')\n# Set the x-axis label\nplt.xlabel('Latency (ms)')\n# Set axis range\nplt.axis([0, 15, 0, 15])\n\nplt.show()\n\nCalculate mean an variance using Numpy&#182;\n\nIf you have only 2 features, you can calculate/estimate mean value as follows:\n\nIn [5]:\n\ndata2 = np.array([[1,2],[4,8],[14,15]])\nm, n = data2.shape\nprint(data2[:,0].sum()/m)\nprint(data2[:,1].sum()/m)\n\nmu1 = np.array([data2[:,0].sum()/m, data2[:,1].sum()/m])\n\n6.333333333333333\n8.333333333333334\n\nOr, you can get advantage of np.mean module:\n\nIn [6]:\n\nmu2 = np.mean(data2, axis=0)\nmu2\n\nOut[6]:\n\narray([6.33333333, 8.33333333])\n\nIn [7]:\n\ndef estimate_gaussian(X):\n\"\"\"\nCalculates mean and variance of all features\nin the dataset",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_3",
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  {
    "text": "Out[6]:\n\narray([6.33333333, 8.33333333])\n\nIn [7]:\n\ndef estimate_gaussian(X):\n\"\"\"\nCalculates mean and variance of all features\nin the dataset\n\nArgs:\nX (ndarray): (m, n) Data matrix\n\nReturns:\nmu (ndarray): (n,) Mean of all features\nvar (ndarray): (n,) Variance of all features\n\"\"\"\n\nm, n = X.shape\nmu = np.mean(X, axis=0)\nvar = np.mean((X-mu)**2, axis=0)\nreturn mu, var\n\nIn [8]:\n\n# Estimate mean and variance of each feature\nmu, var = estimate_gaussian(X_train)\n\nprint(\"Mean of each feature:\", mu)\nprint(\"Variance of each feature:\", var)\n\nMean of each feature: [6.04166667 5.38888889]\nVariance of each feature: [7.52854167 6.0804321 ]\n\nCalculate p_val&#182;\n\nIn [9]:",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_4",
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  {
    "text": "Mean of each feature: [6.04166667 5.38888889]\nVariance of each feature: [7.52854167 6.0804321 ]\n\nCalculate p_val&#182;\n\nIn [9]:\n\ndef multivariate_gaussian(X, mu, var):\n\"\"\"\nComputes the probability\ndensity function of the examples X under the multivariate gaussian\ndistribution with parameters mu and var. If var is a matrix, it is\ntreated as the covariance matrix. If var is a vector, it is treated\nas the var values of the variances in each dimension (a diagonal\ncovariance matrix\n\"\"\"\n\nk = len(mu)\n\nif var.ndim == 1:\nvar = np.diag(var)\n\nX = X - mu\np = (2* np.pi)**(-k/2) * np.linalg.det(var)**(-0.5) * \\\nnp.exp(-0.5 * np.sum(np.matmul(X, np.linalg.pinv(var)) * X, axis=1))\n\nreturn p\n\nIn [10]:",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_5",
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  {
    "text": "return p\n\nIn [10]:\n\ndef visualize_fit(X, mu, var):\n\"\"\"\nThis visualization shows you the\nprobability density function of the Gaussian distribution. Each example\nhas a location (x1, x2) that depends on its feature values.\n\"\"\"\n\nX1, X2 = np.meshgrid(np.arange(0, 20, 0.5), np.arange(0, 20, 0.5))\nZ = multivariate_gaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, var)\nZ = Z.reshape(X1.shape)\n\nplt.plot(X[:, 0], X[:, 1], 'bx')\n\nif np.sum(np.isinf(Z)) == 0:\nplt.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), linewidths=1)\n\n# Set the title\nplt.title(\"The Gaussian contours of the distribution fit to the dataset\")\n# Set the y-axis label\nplt.ylabel('Throughput (mb/s)')\n# Set the x-axis label\nplt.xlabel('Latency (ms)')\n\nIn [11]:",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_6",
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  {
    "text": "In [11]:\n\np = multivariate_gaussian(X_train, mu, var)\nprint(p)\nvisualize_fit(X_train, mu, var)\n\n[0.01437701 0.02233177 0.01464769 0.01160346 0.01349121 0.01768254\n0.01062604 0.01873874 0.02161721 0.01115485 0.01899981 0.00518436\n0.01761434 0.01027897 0.02122577 0.00028315 0.01600785 0.01781667\n0.00788924 0.01768082 0.01761434 0.00323058 0.00514247 0.00494886\n0.0037409 0.00519717 0.02280922 0.01299177 0.00300614 0.00274066\n0.01888504 0.01867716 0.01027897 0.00025977 0.0126335 0.00251688]\n\nSelect the threshold $\\epsilon$&#182;\n\nWe will use F1 score on cross validation set.\n\nIf an example $x$ has a probability less than epsilon: $p(x) < \\varepsilon$, then it is classified as an anomaly.\n\nprec = TP / (TP + FP)\nrecall = TP/ (TP + FN)\n\nF1 = (2 prec recall) / (prec + recall)\n\nIn [12]:",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_7",
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  {
    "text": "prec = TP / (TP + FP)\nrecall = TP/ (TP + FN)\n\nF1 = (2 prec recall) / (prec + recall)\n\nIn [12]:\n\n#example\np_val = np.array([0.2, 0.35, 0.9, 0.1, 0.5])\ny_t = np.array([1,0,0,1,0])\nep = 0.4\npred = np.array(p_val<ep)\npred\n\nOut[12]:\n\narray([ True, True, False, True, False])\n\nIn [13]:\n\nTP = ((pred == 1) & (y_t == 1)).sum()\nFP = ((pred == 1) & (y_t == 0)).sum()\nprecision = TP / (TP+FP)\nprecision\n\nOut[13]:\n\n0.6666666666666666\n\nIn [14]:\n\ndef select_threshold(y_val, p_val):\n\"\"\"\nFinds the best threshold to use for selecting outliers\nbased on the results from a validation set (p_val)\nand the ground truth (y_val)\n\nArgs:\ny_val (ndarray): Ground truth on validation set\np_val (ndarray): Results on validation set",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_8",
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  {
    "text": "Args:\ny_val (ndarray): Ground truth on validation set\np_val (ndarray): Results on validation set\n\nReturns:\nepsilon (float): Threshold chosen\nF1 (float): F1 score by choosing epsilon as threshold\n\"\"\"\n\nbest_epsilon = 0\nbest_F1 = 0\nF1 = 0\n\nstep_size = (max(p_val) - min(p_val)) / 1000\n\nfor epsilon in np.arange(min(p_val), max(p_val), step_size):\n\npred = np.array(p_val < epsilon)\n\nTP = ((pred == 1) & (y_val == 1)).sum()\nFP = ((pred == 1) & (y_val == 0)).sum()\nFN = ((pred == 0) & (y_val == 1)).sum()\nprecision = TP / (TP+FP)\nrecall = TP/ (TP+FN)\n\nF1 = (2 * precision * recall) / (precision + recall)\n\nif F1 > best_F1:\nbest_F1 = F1\nbest_epsilon = epsilon\n\nreturn best_epsilon, best_F1\n\nIn [15]:\n\np_val = multivariate_gaussian(X_val, mu, var)\nepsilon, F1 = select_threshold(y_val, p_val)",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_9",
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  {
    "text": "return best_epsilon, best_F1\n\nIn [15]:\n\np_val = multivariate_gaussian(X_val, mu, var)\nepsilon, F1 = select_threshold(y_val, p_val)\n\nprint('Best epsilon found using cross-validation: %e' % epsilon)\nprint('Best F1 on Cross Validation Set: %f' % F1)\n\nBest epsilon found using cross-validation: 7.227568e-04\nBest F1 on Cross Validation Set: 0.909091\n\n<ipython-input-14-fd63c684c3b9>:29: RuntimeWarning: invalid value encountered in long_scalars\nprecision = TP / (TP+FP)\n\nFind outliers on training set&#182;\n\nIn [16]:\n\n# Find the outliers in the training set\noutliers = p < epsilon\n\n# Visualize the fit\nvisualize_fit(X_train, mu, var)\n\n# Draw a red circle around those outliers\nplt.plot(X_train[outliers, 0], X_train[outliers, 1], 'ro',\nmarkersize= 10,markerfacecolor='none', markeredgewidth=2)",
    "source": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html",
    "type": "html_notebook",
    "chunk_id": "notebook: 2022-11-05-Anomaly_Detection_with_Numpy.html_10",
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  {
    "text": "CV - Pelin Balci\n\n• 🧠\">\n\n•\n\n🧠 Tiny AI World\n\nHome\nAll Notes\nProjects\nCV\nAbout\n\n🌙\n\nCurriculum Vitae\n\nPelin Balci\n\nMSc Industrial Engineer · Senior Lead Machine Learning Engineer\n\nI build production-grade machine learning systems and research Retrieval Augmented Generation, Knowledge Graphs, TinyML, NLP, Large Language Models, and Reinforcement Learning. With a foundation in industrial engineering and a passion for open-source collaboration, I deliver applied AI solutions that balance rigor and real-world impact.\n\nRAG & Knowledge Graphs\nNLP & LLMs\nTinyML\nReinforcement Learning\n\nPortfolio ↗\n\nLocation\nİstanbul, Turkey\n\nExperience\n10+ years\n\nFocus\nML Engineering & Optimization\n\nCommunity\nAYA contributor & mentor\n\nContact\n\nConnect across professional and publishing platforms.",
    "source": "CV / Resume",
    "type": "html_page",
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  {
    "text": "Focus\nML Engineering & Optimization\n\nCommunity\nAYA contributor & mentor\n\nContact\n\nConnect across professional and publishing platforms.\n\nLinkedIn\nGitHub\nWebsite\nMedium\nYouTube\n\nSkills & Toolbox\n\nHands-on expertise spanning operations research, ML systems, and embedded AI.\n\nCore Domains\n\n• Operations Research, Mathematical Modeling, Simulation\n•\n\n• Machine Learning, Deep Learning, Reinforcement Learning\n•\n\n• Tiny Machine Learning, NLP, LLMs, Prompt Engineering\n•\n\n• Transformers, NER Models, Retrieval Augmented Generation\n•\n\nSoftware & Platforms\n\n• Python (Pandas, NumPy, scikit-learn, Matplotlib, PyTorch, TensorFlow, TensorFlow Lite, SimPy, Streamlit)\n•\n\n• LLM tooling: llama-index, LangChain\n•\n\n• SQL, GAMS, Power BI\n•\n\nExperience",
    "source": "CV / Resume",
    "type": "html_page",
    "chunk_id": "CV / Resume_1",
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  {
    "text": "• LLM tooling: llama-index, LangChain\n•\n\n• SQL, GAMS, Power BI\n•\n\nExperience\n\nFrom ML engineering leadership to analytics and optimization roles.\n\n2024 – Present\n\nSenior Lead Machine Learning Engineer · Arçelik Global\n\n• Text classification pipelines with AWS deployment.\n•\n\n• RAG initiatives: knowledge graphs and metadata extraction.\n•\n\n2023\n\nLead Data Scientist / ML Engineer · SabancıDx\n\n• Azure OpenAI GPT project compiling keynote speech content.\n•\n\n• Web/App automation using NLP and GPT models.\n•\n\n• Air handling unit energy optimization with reinforcement learning.\n•\n\n2022\n\nSr. Data Scientist / ML Engineer · SabancıDx\n\n• Retail demand prediction and productionization.\n•\n\n2020 – 2022\n\nData Scientist / ML Engineer · SabancıDx",
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    "text": "• Retail demand prediction and productionization.\n•\n\n2020 – 2022\n\nData Scientist / ML Engineer · SabancıDx\n\n• Production breakage prediction and sensor recommendation.\n•\n\n• IOT air quality product; monitoring with Power BI and Python.\n•\n\n• Energy imbalance direction prediction on Databricks.\n•\n\n• Delivery optimization: simulation with SimPy and staffing optimization with Gurobi.\n•\n\n2019 – 2020\n\nSr. Data Analyst · Adphorus (Sojern)\n\n• Algorithm performance reporting and A/B testing for digital marketing.\n•\n\n2017 – 2019\n\nCommodity Portfolio Management Specialist · Enerjisa Sales\n\n• Built mark-to-market reporting, hedging strategies, and demand analyses.\n•\n\n2015 – 2017\n\nPortfolio Optimization Specialist · Enerjisa Trading",
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    "text": "• Built mark-to-market reporting, hedging strategies, and demand analyses.\n•\n\n2015 – 2017\n\nPortfolio Optimization Specialist · Enerjisa Trading\n\n• Power generation optimization tools and long-term portfolio planning.\n•\n\n2013 – 2015\n\nProject Management Specialist · MilSOFT Software Technologies\n\n• Software project management (CMMI5, Agile/Scrum), risk management, and HR analytics.\n•\n\n2010 – 2012\n\nTeaching Assistant · Bilkent University\n\n• Industrial Engineering Department teaching and academic support.\n•\n\nProjects\n\nSelected personal and live applications.\n\nLive Apps\n\n• Apps Portfolio\n•\n\n• Movie Recommendation\n•\n\n• EasyOCR (Hugging Face Space)\n•\n\n• Language Detection (Hugging Face Space)\n•\n\nResearch & Engineering\n\n• Information Extraction from Pathology Reports\n•",
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  {
    "text": "• Language Detection (Hugging Face Space)\n•\n\nResearch & Engineering\n\n• Information Extraction from Pathology Reports\n•\n\n• Hugging Face Portfolio\n•\n\n• Occupancy Detection (TensorFlow Lite & Neuton AI)\n•\n\n• Audio Classifier with Edge Impulse\n•\n\n• Motion Classifier with Edge Impulse\n•\n\nEducation\n\nFormal studies in industrial engineering and optimization.\n\nMaster of Science, Industrial Engineering\n\nBilkent University Graduate School of Engineering and Science (2010 – 2013) — GPA: 3.09 / 4.00\n\nMaster’s Thesis: Analysis of Locations of Existing Fire Stations in Ankara in Comparison to Optimized Locations\n\nBachelor of Science, Industrial Engineering\n\nGazi University Engineering and Architecture Faculty (2005 – 2010) — GPA: 3.44 / 4.00\n\nCertifications & Trainings",
    "source": "CV / Resume",
    "type": "html_page",
    "chunk_id": "CV / Resume_5",
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    "text": "Gazi University Engineering and Architecture Faculty (2005 – 2010) — GPA: 3.44 / 4.00\n\nCertifications & Trainings\n\nContinuous learning across deep learning, embedded ML, machine learning, and leadership.\n\nDeep Learning & Generative AI\n\n• AWS AI Practitioner Certification, Nov 2025\n•\n\n• OxML 2025\n•\n\n• DeepLearning.AI 2024 highlights\n•\n\n• Retrieval Optimization to Vector Quantization; Knowledge Graphs for RAG; Quality & Safety for LLM Applications\n•\n\n• Building Applications with Vector Databases\n•\n\n• Google Cloud Generative AI, Introduction to LLMs (Coursera, 2023)\n•\n\n• Generative AI with Large Language Models (Coursera, 2023)\n•\n\n• ChatGPT Teach-Out (University of Michigan, 2023)\n•\n\n• Deep Learning Specialization modules on NLP, sequence models, CNNs, TensorFlow (2021–2022)\n•",
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  {
    "text": "• ChatGPT Teach-Out (University of Michigan, 2023)\n•\n\n• Deep Learning Specialization modules on NLP, sequence models, CNNs, TensorFlow (2021–2022)\n•\n\n• CS231n Convolutional Neural Networks for Visual Recognition (Stanford)\n•\n\nEmbedded & Classical ML\n\n• Applications of TinyML; Fundamentals of TinyML (HarvardX, 2022)\n•\n\n• Introduction to Embedded Machine Learning (Edge Impulse, 2021)\n•\n\n• Machine Learning Specialization (DeepLearning.AI, 2022)\n•\n\n• Unsupervised Learning, Recommenders, Reinforcement Learning (DeepLearning.AI, 2022)\n•\n\n• AWS Machine Learning Foundations (Udacity, 2020)\n•\n\n• Google Machine Learning Crash Course with TensorFlow API (2020)\n•\n\n• Udacity Intro to Machine Learning; Bayesian Machine Learning via Python (Udemy)\n•",
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  {
    "text": "• Udacity Intro to Machine Learning; Bayesian Machine Learning via Python (Udemy)\n•\n\n• Advanced Data Analytics, Data Scientist Track (Sabancı University, 2018)\n•\n\nLeadership & Other\n\n• The Science of Stem Cells (AMNH, 2023)\n•\n\n• Self Awareness and the Effective Leader (Rice University, 2022)\n•\n\n• Discrete Optimization (University of Melbourne, 2020)\n•\n\n• Udacity Nanodegrees: Bertelsmann Data Analyst; Google Android Basics\n•\n\n• Intermediate/Intro to Python for Data Science (DataCamp, 2017)\n•\n\n• Data Science Orientation (edX, 2017)\n•\n\nAwards & Scholarships\n\n• Advanced Data Analytics (Data Scientist Track) — 3rd Prize, Sabancı University (2018)\n•\n\n• Brabant Water Demand Prediction — 3rd Prize, JADS 7th Data Challenge Week (2019)\n•",
    "source": "CV / Resume",
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  {
    "text": "• Brabant Water Demand Prediction — 3rd Prize, JADS 7th Data Challenge Week (2019)\n•\n\n• Bilkent University Graduate School of Engineering and Science Scholarship (2010–2012)\n•\n\n• TÜBİTAK Graduate Scholarship (2010–2012)\n•\n\nLanguages & Interests\n\nLanguages\n\nEnglish (Advanced), Spanish (Beginner), German (Beginner)\n\nInterests\n\nMinimalism, Cycling, Lindy Hop & Solo Jazz, Chess, Solar car racing (TÜBİTAK Formula-G), Tech community building.\n\n&copy; 2025 Tiny AI World. Free and Open Source.\n\nGitHub •\nLicense •\nAbout",
    "source": "CV / Resume",
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  {
    "text": "Projects - Tiny AI World\n\n• 🧠\">\n\n•\n\n🧠 Tiny AI World\n\nHome\nAll Notes\nProjects\nCV\nAbout\n\n🌙\n\nProjects\n\nExperiments, implementations, and real-world applications\n\nFEATURED\n\nTiny AI World\n\nAn interactive platform for organizing and visualizing learning materials.\nBuilt with D3.js, this project demonstrates the power of connected knowledge and visual learning. Thanks to Claude and ChatGPT.\n\nD3.js\nJavaScript\nGitHub Pages\n\nView Project\n\nGitHub Repo\n\nMy Awesome Projects\n\n🤖\n\nEasy OCR\n\nThis project aims to use EasyOCR to detect the text in the images. It is deployed to\nHuggingFace Space and the UI is designed with Streamlit.\n\nPython\nStreamlit\nEasyOCR\n\nLive Demo →\n\n📊\n\nLanguage Detection\n\nI've learned how to use HuggingFace and Streamlit with this project. It is a simple language detection UI.",
    "source": "Projects Page",
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  {
    "text": "Live Demo →\n\n📊\n\nLanguage Detection\n\nI've learned how to use HuggingFace and Streamlit with this project. It is a simple language detection UI.\n\nPython\nStreamlit\n\nLive Demo →\n\n🎬\n\nMovie Recommendation System\n\nA personalized movie recommendation engine using collaborative filtering with matrix factorization. Rate movies you've seen and get tailored suggestions based on learned preferences.\n\nPython\nTensorFlow\nStreamlit\n\nView Code →\n\n💡\n\nView My GitHub\n\nYou may come across some interesting works.\n\nMy Portfolio on GitHub →\n\nGoogle Colab Notebooks\n\nInteractive tutorials and experiments you can run in your browser\n\nQuantization\n\nQuantization with PyTorch and TensorFlow\n\nOpen in Colab\n\nDeep Dream from Scratch\n\nUnderstanding how neural networks \"see\" through feature visualization with PyTorch",
    "source": "Projects Page",
    "type": "html_page",
    "chunk_id": "Projects Page_1",
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    "text": "Open in Colab\n\nDeep Dream from Scratch\n\nUnderstanding how neural networks \"see\" through feature visualization with PyTorch\n\nOpen in Colab\n\nReach me\n\nif you have any questions about my projects!\n\nLinkedin ->\n\n&copy; 2025 Tiny AI World. Free and Open Source.\n\nGitHub •\nLicense •\nAbout",
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    "text": "About - Tiny AI World\n\n• 🧠\">\n\n•\n\n🧠 Tiny AI World\n\nHome\nAll Notes\nProjects\nCV\nAbout\n\n🌙\n\nAbout Tiny AI World\n\nA personal platform for exploring AI, Machine Learning, and beyond.\n\n🌍 What is This?\n\nTiny AI World is an interactive platform that visualizes the connections between\ndifferent topics in AI, machine learning, programming, and data science. Unlike traditional\nnote-taking apps, this platform shows how concepts relate to each other, making it easier\nto understand the bigger picture.\n\nEvery node in the graph represents a topic, and the connections show how these topics\nare related. Click on any node to dive deeper into that subject.\n\n🎯 The Mission",
    "source": "About Page",
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    "text": "🎯 The Mission\n\nMy goal is to create a free, open-source learning resource that helps people understand\ncomplex AI/ML concepts through connected, bite-sized notes. Knowledge is better when it's\nshared, and learning is more effective when you can see the relationships between ideas.\n\n👋 About Me\n\nHi! I'm Pelin, an Industrial Engineer with MSc and Machine Learning Engineer passionate about\nartificial intelligence, machine learning, and building tools that help people learn.\n\nI created Tiny AI World to organize my own learning journey and share knowledge with others.\nThrough this platform, I document what I learn, build connections between concepts, and\nhopefully help others on their learning path.\n\nConnect with me:\n\nGitHub\n\nLinkedIn\n\nTwitter\n\nMedium\n\n🛠️ Built With\n\nD3.js",
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    "text": "Connect with me:\n\nGitHub\n\nLinkedIn\n\nTwitter\n\nMedium\n\n🛠️ Built With\n\nD3.js\n\nInteractive graph visualization\n\nVanilla JS\n\nLightweight and fast\n\nMarkdown\n\nEasy content creation\n\nGitHub Pages\n\nFree hosting\n\n✨ Features\n\n• Interactive knowledge graph visualization\n•\n\n• Search across all topics\n•\n\n• Category-based filtering\n•\n\n• Dark/Light mode\n•\n\n• Mobile responsive design\n•\n\n• Easy note creation with Markdown\n•\n\n• Automatic relationship mapping\n•\n\n• Free and open source\n•\n\n🤝 Contributing\n\nThis is an open-source project! If you find errors, have suggestions, or want to contribute\ncontent, feel free to:\n\n• Open an issue on GitHub\n•\n\n• Submit a pull request\n•\n\n• Share feedback via email\n•\n\n• Fork the project for your own knowledge graph\n•\n\n📄 License",
    "source": "About Page",
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    "text": "• Open an issue on GitHub\n•\n\n• Submit a pull request\n•\n\n• Share feedback via email\n•\n\n• Fork the project for your own knowledge graph\n•\n\n📄 License\n\nThis project is licensed under the MIT License. Feel free to use, modify, and share!\n\nView License\n\nReady to Explore?\n\nDive into the knowledge graph and discover how topics connect!\n\nExplore the Graph\n\n&copy; 2025 Tiny AI World. Free and Open Source.\n\nGitHub •\nLicense •\nAbout",
    "source": "About Page",
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