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Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article
Ahmed Gad
https://towardsdatascience.com/introduction-to-optimization-with-gene... | Introduction to Optimization with Genetic Algorithm | [
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There are many deep learning models specialized in solving many tasks. Here we discuss
Pranoy Radhakrishnan
https://towardsdatascience.com/introduction-to-recurrent-neural-network-27202c3945f3 | Introduction to Recurrent Neural Network | [
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Analyse your conversation history on Telegram programatically
Jiayu Yi
https://towardsdatascience.com/introduction-to-the-telegram-api-b0cd220dbed2 | Introduction to the Telegram API | [
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The main goal of this guide is to provide intuition about theory, techniques and
Egor Dezhic
https://towardsdatascience.com/introductory-guide-to-artificial-intelligence-11fc04cea042 | Introductory Guide to Artificial Intelligence | [
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Exploring the strong visual hierarchies that
Irhum Shafkat
https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1 | Intuitively Understanding Convolutions for Deep Learning | [
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And why theyre so useful in creating your own
Irhum Shafkat
https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf | Intuitively Understanding Variational Autoencoders | [
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Yesterday during Google IO a project called Duplex to interface between a request to a Google
Chris Butler
https://towardsdatascience.com/is-google-duplex-ethical-and-moral-f66a23637640 | Is Google Duplex ethical and moral? | [
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There was a recent blog post on mental models for deep learning drawing parallels from optics [link]. We all have intuitions for few models but it is hard to put it in words, I believe it is necessary to work collectively for this mental model.
Nishant Nikhil
https://towardsdatascience.com/is-relu-after-sigmoid-bad-661... | Is ReLU after Sigmoid bad? | [
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My team is very serious about Christmas and the gifts we get. So, we wanted to track Santa Claus and know when he delivers our presents. We decided to put a camera in our chimneys and find him. Recently we saw this post about how to train Tensorflows object-detection API for your own
Varun Vohra
https://towardsdatascie... | Is Santa Claus Real? | [
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Or Are You Just Happy to See Me?
Velocity
https://towardsdatascience.com/is-that-ai-in-your-pocket-97cb245f8ee9 | Is That AI in Your Pocket? | [
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Artificial intelligence (AI) is no longer the stuff of science fiction alone. Its widespread in business and in our homesmany people have smart phone conversations with Siri or rely on the assistance of Amazons Alexa. But, does
Jeff Meade
https://towardsdatascience.com/is-your-business-ready-for-artificial-intelligence... | Is Your Business Ready for Artificial Intelligence (AI)? | [
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What do you think of when you think of movies? About storylines and characters and dialogues
Shamli Prakash
https://towardsdatascience.com/its-showtime-folks-d9fd274810c8 | Stories in Data: Its Showtime Folks! | [
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One of the leading figures in t he world of Artificial Intelligence
Andres Vourakis
https://towardsdatascience.com/join-the-aiforeveryone-initiative-86bae2dfc13a | Join the #AIforEveryone initiative | [
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Towards Data Science provides a platform for thousands of people to exchange ideas and to expand our
TDS Team
https://towardsdatascience.com/join-us-as-an-editorial-associate-of-towards-data-science-766cdd74d13e | Join us as an Editorial Associate | [
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Toxic-comments classification.
Kartik Nooney
https://towardsdatascience.com/journey-to-the-center-of-multi-label-classification-384c40229bff | Deep dive into multi-label classification..! | [
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GitHub link of the project. LinkedIn profile.
Kartik Nooney
https://towardsdatascience.com/judging-a-book-by-its-cover-1365d001ef50 | Judging a book by its cover..! | [
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Motivation
Tanbal
https://towardsdatascience.com/jupyter-data-science-stack-docker-in-under-15-minutes-19d8f822bd45 | Jupyter Data Science Stack + Docker in under 15 minutes | [
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Youll probably already know the Jupyter notebooks pretty wellits probably one of the most well-known parts of the Jupyter ecosystem! If you havent explored the ecosystem yet or if you simply want to know more about it, dont hesitate to go and explore it here!.
Karlijn Willems
https://towardsdatascience.com/jupyter-note... | Jupyter Notebook Cheat Sheet | [
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The Jupyter Notebook is an incredibly powerful tool for interactively developing and
Benjamin Pryke
https://towardsdatascience.com/jupyter-notebook-for-beginners-a-tutorial-f55b57c23ada | Jupyter Notebook for Beginners: A Tutorial | [
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Attend a day-long exploration of Jupyters best practices and practical use cases in business and industry.
Formulated.by
https://towardsdatascience.com/jupyter-pop-up-coming-to-boston-on-march-21-425322c61a1f | Jupyter Pop-up coming to Boston on March 21 | [
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How many times have you wandered to a store, and told them, you need a skirt. But not
Sanchit Aggarwal
https://towardsdatascience.com/just-pile-of-stuff-and-magic-of-ai-8bd651428b9d | Just a Pile of Stuff and the Magic of AI | [
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A walk through of my approach
Chris Dinant
https://towardsdatascience.com/kaggle-tensorflow-speech-recognition-challenge-b46a3bca2501 | Kaggle Tensorflow Speech Recognition Challenge | [
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I was given a project at General Assembly where I was given physical breast tumor data, and was asked to predict whether the tumor was malignant or benign. I was specifically asked to compare two different modeling techniques in this assignment
Brendan Bailey
https://towardsdatascience.com/keep-it-simple-stupid-lesson-... | Keep It Simple Stupid: Lesson in Model Selection | [
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To make sure your customer gets the full benefit of your model, use one of the go-to tools of Six Sigma and manufacturing QA to
Robert de Graaf
https://towardsdatascience.com/keeping-the-gains-1249d1a8b1d5 | Keeping the gains | [
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Dont lose your users goodwill by setting unrealistic expectations
Robert de Graaf
https://towardsdatascience.com/keeping-your-customers-faith-7ebbf2bb2610 | Keep your users trust | [
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A great model needs love and attention if it is stay as useful as it was on day one for its whole
Robert de Graaf
https://towardsdatascience.com/keeping-your-production-model-fresh-9cf91e785630 | Keeping your production model fresh | [
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In my previous article, we discussed about the chemistry between Big Data and Machine Learning. We also reach to the
Krishna Kumar Tiwari
https://towardsdatascience.com/know-your-data-part-1-c6bd56553ae8 | Know Your Data | Part 1 | [
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A needed tool in your data science toolbox
Marc-Olivier Arsenault
https://towardsdatascience.com/kolmogorov-smirnov-test-84c92fb4158d | KOLMOGOROVSMIRNOV TEST | [
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In my last post, I covered the introduction to Regularization in supervised learning models. In this post, lets go over some of the regularization techniques widely used and the key difference between those.
Anuja Nagpal
https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c | L1 and L2 Regularization Methods | [
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Whether youre a new startup or an existing business, heres one way you can get an AI-enabled product
Aaron Edell
https://towardsdatascience.com/launch-with-ai-in-1-week-a4f4f45cc177 | Launch with AI in 1 week or less | [
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Learn more about LDA2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors.
Lars Hulstaert
https://towardsdatascience.com/lda2vec-word-embeddings-in-topic-models-4ee3fc4b2843 | LDA2vec: Word Embeddings in Topic Models | [
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Why Im ditching the library to write a data science blog
William Koehrsen
https://towardsdatascience.com/learn-by-sharing-4461cc93f8c1 | Learn By Sharing | [
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I guarantee you that this short tutorial will save you a TON of time from reading the long documentations. Ready to jump on the Big Data Train? Lets get to it!
Zhen Liu
https://towardsdatascience.com/learn-spark-essentials-in-15-mins-cf1495882ae0 | Learn Spark for Big Data Analytics in 15 mins | [
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I was recently looking for a dataset to perform sentiment analysis on popular pop song lyrics. I went through a lot of sites providing free datasets but didnt find any that met my
Deepal Dsilva
https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5 | Learn To Create Your Own DatasetsWeb Scraping in R | [
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In the ever-changing ecosystem of data science tools, you often find yourself needing to learn
Ben Weber
https://towardsdatascience.com/learning-a-new-data-science-language-aa7656be730a | Learning A New Data Science Language | [
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The premise of meta learning was an intoxicating one to me, when I first of
Cody Marie Wild
https://towardsdatascience.com/learning-about-algorithms-that-learn-to-learn-9022f2fa3dd5 | Learning About Algorithms That Learn to Learn | [
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Its currently an arms race in the tech scene right now with Deep Learning and Artificial
James Lee
https://towardsdatascience.com/learning-artistic-styles-from-images-fa14c60890c1 | Learning Artistic Styles from Images | [
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What are we exactly, whats happening in our very own brain? Is it just a chemical reaction that makes people think? Or is it more than that. We, humans, created so many metrics to solve a lot of problems to survive and help each other. But we still make mistakes like
Vihar Kurama
https://towardsdatascience.com/learning... | Learning humans the machine way. | [
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Some Empirical Result Comparison
Ceshine Lee
https://towardsdatascience.com/learning-note-dropout-in-recurrent-networks-part-3-1b161d030cd4 | [Learning Note] Dropout in Recurrent NetworksPart 3 | [
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In training deep networks, it is helpful to reduce the learning rate as the number of training epochs increases. This is based on the intuition that with a high learning rate, the deep learning model would possess high kinetic energy. As
Shreenidhi Sudhakar
https://towardsdatascience.com/learning-rate-scheduler-d8a5574... | Learning Rate Scheduler | [
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Using real muscles to
Norman Di Palo
https://towardsdatascience.com/learning-to-walk-with-evolutionary-algorithms-applied-to-a-bio-mechanical-model-1ccc094537ce | Learning to walk with evolutionary algorithms applied to a bio-mechanical model | [
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Machine Learning for Product ManagersThe magic of qualitative feedbacks
Shiyu Zhu
https://towardsdatascience.com/lesson-1-for-building-machine-learning-products-the-magic-of-qualitative-feedbacks-3dd63c678063 | [
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I recently finished the first release version of my R package, called sdmbench
Boyan Angelov
https://towardsdatascience.com/lessons-learned-from-building-an-r-package-75d5263d3814 | Lessons Learned from Building an R Package | [
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This article provides an intuitive approach to neural networks and their learning process
Mateusz Dziubek
https://towardsdatascience.com/let-me-introduce-you-to-neural-networks-fedf4253106a | Let me introduce you to neural networks | [
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by using Adversarial Patch
Francesco Zuppichini
https://towardsdatascience.com/lets-fool-a-neural-network-b1cded8c4c07 | Lets fool a neural network! | [
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I remember the book titled Concrete Mathematics by Donald Knuth, which was put forth by one of my math teachers, who thought that there are too much that is being abstracted monotonously and encapsulated into formulas and notations, and this book laid
Mohammed Musfir N N
https://towardsdatascience.com/lets-learn-it-dee... | Lets learn it deep before we play it Deep | [
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Leveraging the Power of AI in Marketing, Now and In the Future
Kerri Hale
https://towardsdatascience.com/leveraging-the-power-of-ai-in-marketing-now-and-in-the-future-42de905e8274 | Leveraging the Power of AI in Marketing, Now and In the Future | [
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Last week I was invited to my PhD school to share my experience leaving Academia
Bruno S nchez-A Nu o
https://towardsdatascience.com/life-after-a-phd-the-option-b-leaving-a18bc89e2d33 | Life after a PhD: the option B, leaving | [
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The Math behind every deep learning program.
Vihar Kurama
https://towardsdatascience.com/linear-algebra-for-deep-learning-506c19c0d6fa | Linear Algebra for Deep Learning | [
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The concepts of Linear Algebra are crucial for understanding the theory behind Machine
Niklas Donges
https://towardsdatascience.com/linear-algebra-for-deep-learning-f21d7e7d7f23 | Basic Linear Algebra for Deep Learning | [
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Linear regression is used for finding linear relationship between target and one or more predictors
Saishruthi Swaminathan
https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86 | Linear RegressionDetailed View | [
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Data science is at its peak, using machine learning models you can do a lot, from predicting stock prices
Abdul Hafeez Abdul Raheem
https://towardsdatascience.com/linear-regression-from-scratch-cd0dee067f72 | Linear Regression from scratch | [
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In linear regression, you are attempting to build a model that allows you to predict the value of new data, given the training data used to train your model. This will become clear as we work through this post.
Dannar Mawardi
https://towardsdatascience.com/linear-regression-in-python-9a1f5f000606 | Linear Regression in Python | [
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Real world problems solved with Math
Carolina Bento
https://towardsdatascience.com/linear-regression-in-real-life-4a78d7159f16 | Linear Regression In Real Life | [
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A statistical case study of the popular sports story
Sayar Banerjee
https://towardsdatascience.com/linear-regression-moneyball-part-1-b93b3b9f5b53 | Linear Regression: MoneyballPart 1 | [
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A statistical case study of the popular sports story
Sayar Banerjee
https://towardsdatascience.com/linear-regression-moneyball-part-2-175a9dc72e89 | Linear Regression: MoneyballPart 2 | [
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My goal is to eventually write out articles like this for other optimization techniques. Lets start with gradient descent. Note: This isnt a comprehensive guide as I skim through a lot of things.
Joseph J. Bautista
https://towardsdatascience.com/linear-regression-using-gradient-descent-in-10-lines-of-code-642f995339c0 | Linear Regression Using Gradient Descent in 10 Lines of Code | [
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Data science is an inter-disciplinary field which contains methods and techniques
Shashank Gupta
https://towardsdatascience.com/list-of-must-read-free-data-science-books-bfae4c5c5a16 | List of MustRead Free Data Science Books | [
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An efficient approach to identifying approximate nearest neighbors.
Santhosh Hari
https://towardsdatascience.com/locality-sensitive-hashing-for-music-search-f2f1940ace23 | Locality Sensitive Hashing for Similar Item Search | [
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Logos sometimes also known as trademark have high importance in todays marketing world. Products
Ankur Singh
https://towardsdatascience.com/logo-detection-in-images-using-ssd-bcd3732e1776 | Logo detection in Images using SSD | [
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A practical application of convolutional neural networks
Kasper Fredenslund
https://towardsdatascience.com/looking-at-german-traffic-signs-a03eb49def72 | Looking at German Traffic Signs | [
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A more efficient loss function for Siamese NN
Marc-Olivier Arsenault
https://towardsdatascience.com/lossless-triplet-loss-7e932f990b24 | Lossless Triplet loss | [
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In previous articles on how to hire an AI consultant, and how to price an AI project, I tried to give you a sense of how things work in the AI consulting space. I also gave you a sense of the special challenges faced by many
Daniel Shapiro, PhD
https://towardsdatascience.com/low-budgets-and-high-expectations-machine-le... | Low Budgets and High Expectations: Machine Learning Startups | [
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INT8 optimization for Convolutional Neural Networks using Nvidia
Vignesh Ungrapalli
https://towardsdatascience.com/low-precision-inference-with-tensorrt-6eb3cda0730b | Low Precision Inference with TensorRT | [
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What is LSTM?
Ravindra Kompella
https://towardsdatascience.com/lstm-nuggets-for-practical-applications-5beef5252092 | LSTMnuggets for practical applications | [
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Sentiment analysis: 10 applications and 4 services
Sebastian Kwiatkowski
https://towardsdatascience.com/machine-learning-as-a-service-487e930265b2 | Machine Learning as a Service: Part 1 | [
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guided by Sukant Khurana
Divyansh Dwivedi
https://towardsdatascience.com/machine-learning-for-beginners-d247a9420dab | Machine Learning For Beginners | [
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About one in seven U.S. adults has diabetes now, according to the Centers for Disease Control and
Susan Li
https://towardsdatascience.com/machine-learning-for-diabetes-562dd7df4d42 | Machine Learning for Diabetes | [
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This year NIPS had two healthcare/medicine related workshops. Machine Learning for
Isaac Godfried
https://towardsdatascience.com/machine-learning-for-healthcare-at-nips-c96127bbbae | Machine Learning for Healthcare at NIPS | [
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Machine learning(ML) and AI are hot topics these days. So, I find a lot of product managers and would-be product managers come up to me and ask how they can become better ML PMs. Since the intersection of machine learning
Uzma Barlaskar
https://towardsdatascience.com/machine-learning-for-product-managers-part-i-problem... | Machine Learning for Product Managers Part IProblem Mapping | [
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The first note focused on what problems are best suited for application of machine learning techniques. The second
Uzma Barlaskar
https://towardsdatascience.com/machine-learning-for-product-managers-part-iii-caveats-79803a7548ef | Machine Learning for Product Managers Part IIICaveats | [
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How AI helps traders make better decisions & improve high-frequency
Markus Schmitt
https://towardsdatascience.com/machine-learning-for-trading-e2a5275b6fe | Machine Learning for Trading | [
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I trained a Support Vector Machine (SVM) model to detect moving vehicles on the road. This
Moataz Elmasry
https://towardsdatascience.com/machine-learning-for-vehicle-detection-fd0f968995cf | Machine Learning for Vehicle Detection | [
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Attributes and patterns
Sebastian Kwiatkowski
https://towardsdatascience.com/machine-learning-from-scratch-part-1-76603dececa6 | Machine Learning From Scratch: Part 1 | [
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Collections and data
Sebastian Kwiatkowski
https://towardsdatascience.com/machine-learning-from-scratch-part-2-99ce4c78a3cc | Machine Learning From Scratch: Part 2 | [
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Arrays and representations
Sebastian Kwiatkowski
https://towardsdatascience.com/machine-learning-from-scratch-part-3-ed572330367d | Machine Learning From Scratch: Part 3 | [
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Functions and classification
Sebastian Kwiatkowski
https://towardsdatascience.com/machine-learning-from-scratch-part-4-10117c005a28 | Machine Learning From Scratch: Part 4 | [
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In my previous post I outlined how machine
Conor McDonald
https://towardsdatascience.com/machine-learning-fundamentals-ii-neural-networks-f1e7b2cb3eef | Machine learning fundamentals (II): Neural networks | [
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**This is part one of a series
Conor McDonald
https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220 | Machine learning fundamentals (I): Cost functions and gradient descent | [
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There is a lot to be gained for finance businesses form applying AI. Andin factplenty are already doing
Markus Schmitt
https://towardsdatascience.com/machine-learning-in-finance-2074bc6bf3da | Machine Learning in Finance | [
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Machine learning in finance may work magic, even though there is no magic behind
Konstantin Didur
https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56 | Machine learning in finance: Why, what & how | [
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Learning the Kaggle Environment and an
William Koehrsen
https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426 | Machine Learning Kaggle Competition Part One: Getting Started | [
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Getting the most out of a machine
William Koehrsen
https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507 | Machine Learning Kaggle Competition: Part Three Optimization | [
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Feature engineering, feature selection, and
William Koehrsen
https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8 | Machine Learning Kaggle Competition Part Two: Improving | [
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Probabilistic models
Conor Dewey
https://towardsdatascience.com/machine-learning-madness-predicting-every-ncaa-tournament-matchup-7d9ce7d5fc6d | Machine Learning Madness: Predicting Every NCAA Tournament Matchup | [
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In this episode of AI Adventures, we will attempt to go through an entire machine learning workflow into
Yufeng G
https://towardsdatascience.com/machine-learning-meets-fashion-48ee8f6541ad | Machine Learning Meets Fashion | [
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Image Credit : toshistats.net
Pramod Chandrayan
https://towardsdatascience.com/machine-learning-part-3-logistics-regression-9d890928680f | Machine Learning Part 3 : Logistic Regression | [
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Supervised and Unsupervised Machine Learning
Anuja Nagpal
https://towardsdatascience.com/machine-learning-quick-reference-card-cf92f6accd08 | Machine Learning Quick Reference Card | [
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A few days ago google engineers posted a huge manual on how to build great ML products(and, by
Egor Dezhic
https://towardsdatascience.com/machine-learning-rules-in-a-nutshell-dfc3e6839163 | Machine Learning Rules in a Nutshell | [
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A time might arise were youre going to need to predict using rotational data
Zack Akil
https://towardsdatascience.com/machine-learning-tip-using-rotational-data-b67ded0a33ad | Machine Learning Tip : Using Rotational Data | [
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I like the word heterodox. It is a cool way of saying thinking outside the box, and against the
Daniel Shapiro, PhD
https://towardsdatascience.com/machine-learning-use-small-words-5cc8f34a5964 | Machine Learning: Use Small Words | [
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They are not the samebut are often used interchangeably
Markus Schmitt
https://towardsdatascience.com/machine-learning-vs-artificial-intelligence-192391ce6caf | Machine Learning vs. Artificial Intelligence | [
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Machine LearningWhat it is and why it should interest you!
Blake Lawrence
https://towardsdatascience.com/machine-learning-what-it-is-and-why-it-should-interest-you-97fcf3f68d04 | [
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Thanks all for appreciating and encouraging me for my last article Machine Learning : What &
Pramod Chandrayan
https://towardsdatascience.com/machine-learning-what-why-part-2-abbfdc28c26e | Machine Learning : What & Why Part 2 | [
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Recently, a lot of people started asking me about what machine learning is all about
Harveen Singh
https://towardsdatascience.com/machine-learning-what-why-when-and-how-9a2f244647a4 | Machine Learning- What, Why, When and How? | [
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Easy and robust methodology
Tirthajyoti Sarkar
https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49 | Machine Learning with Python: Easy and robust method to fit nonlinear data | [
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The ability to discern a things distinguishing features is a fundamental aspect of
gk_
https://towardsdatascience.com/machine-reasoning-distinguishing-features-beff5159d957 | Machine Reasoning: distinguishing features | [
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An attempt at writing style transfer
Ludi Rehak
https://towardsdatascience.com/machine-translation-to-shakespearian-english-189c8690b252 | Machine Translation to Shakespearian English | [
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Avoid the distraction of the next shiny thing
Robert de Graaf
https://towardsdatascience.com/magpie-data-science-6cde66b95dde | Magpie Data Science | [
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Multi-pose estimation is currently a state-of-the-art deep learning approach in computer vision
Anson Wong
https://towardsdatascience.com/making-bobblehead-animations-using-deep-learning-1df2cb004429 | Deep Learning Bobblehead Animations | [
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0.08300740271806717,
-0.03842480480670929,
0.24855196475982666,
0.1238010823726654,
0.3587952256202698,
0.3347196578979492,
0.18530157208442688,
-0.4371173083782196,
-0.09301453083753586,
-0.35009488463401794,
0.13762953877449036,
-0.07418660819530487,
-0.576274931430... |
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