bigcode/jupyter-parsed · Datasets at Hugging Face

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[ [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "#!pip install --upgrade tables", "_____no_output_____" ], [ "cd \"/content/drive/My Drive/Colab Notebooks/dw_matrix/matrix_two/dw_matri...

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[ [ [ "# 1- Class Activation Map with convolutions\n\nIn this firt part, we will code class activation map as described in the paper [Learning Deep Features for Discriminative Localization](http://cnnlocalization.csail.mit.edu/)\n\nThere is a GitHub repo associated with the paper:\nhttps://github.com/zhoubo...

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[ [ [ "# linear equations", "_____no_output_____" ], [ "# SolveLinearSystem.py\n# Code to read A and b\n# Then solve Ax = b for x by Gaussian elimination with back substitution", "_____no_output_____" ], [ "# linearsolver with pivoting adapted from \n# https://stackov...

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[ [ [ "# Passive Membrane Tutorial", "_____no_output_____" ], [ "This is a tutorial which is designed to allow users to explore the passive responses of neuron membrane potentials and how it changes under various conditions such as current injection, ion concentration (both inside and ou...

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[ [ [ "# Circuit visualize\n\nこのドキュメントでは scikit-qulacs に用意されている量子回路を可視化します。\nscikitqulacsには現在、以下のような量子回路を用意しています。\n- create_qcl_ansatz(n_qubit: int, c_depth: int, time_step: float, seed=None): [arXiv:1803.00745](https://arxiv.org/abs/1803.00745)\n- create_farhi_neven_ansatz(n_qubit: int, c_depth: int, seed:...

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[ [ [ "# TalkingData: Fraudulent Click Prediction\n\n\n\n\n", "_____no_output_____" ], [ "In this notebook, we will apply various boosting algorithms to solve an interesting classification problem from the domain of 'digital fraud'.\n\nThe analysis is divided into the following sections:...

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[ [ [ "# Topic 2: Neural network\n\n## Lesson 1: Introduction to Neural Networks\n", "_____no_output_____" ], [ "### 1. AND perceptron\n\nComplete the cell below:\n", "_____no_output_____" ] ], [ [ "import pandas as pd\n\n# TODO: Set weight1, weight2, and bias\nwe...

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[ [ [ "# lab1.py \n\n#You should start here when providing the answers to Problem Set 1.\n#Follow along in the problem set, which is at:\n#http://ai6034.mit.edu/fall12/index.php?title=Lab_1\n\n# Import helper objects that provide the logical operations\n# discussed in class.\nfrom production import IF, AND,...

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[ [ [ "# AHDB wheat lodging risk and recommendations\nThis example notebook was inspired by the [AHDB lodging practical guidelines](https://ahdb.org.uk/knowledge-library/lodging): we evaluate the lodging risk for a field and output practical recommendations. We then adjust the estimated risk according to th...

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[ [ [ "import numpy as np\nimport pandas as pd", "_____no_output_____" ], [ "titanic_df = pd.read_csv('titanic_train.csv')\nprint('단일 컬럼 데이터 추출:\\n', titanic_df['Pclass'].head(3))\nprint('\\n여러 컬럼들의 데이터 추출:\\n', titanic_df[['Survived', 'Pclass']].head(3))\n\n# 아래처럼 코딩하는건 좋지 않다.\n# 차라리 Bo...

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[ [ [ "import pandas as pd\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "data = pd.read_csv(\"car.data\", header = None)", "_____no_output_____" ], [ "data.columns =[\"Price\", \"Maintenance Cost\", \"Number of Doors\", \"Capacity\", \"Size of Luggage...

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[ [ [ "# Modeling and Simulation in Python\n\nCase study.\n\nCopyright 2017 Allen Downey\n\nLicense: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n", "_____no_output_____" ] ], [ [ "# Configure Jupyter so figures appear in the notebook...

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[ [ [ "# Python Solution for Hackerrank By Viraj Shetty", "_____no_output_____" ], [ "## Hello World", "_____no_output_____" ] ], [ [ "print(\"Hello, World!\")", "_____no_output_____" ] ], [ [ "## Python If-Else", "_____no_output_____...

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[ [ [ "# Stock walk\n\nThis notebook shows how a Python class can inherit from an interface of an extension module (that is, a class in C++).", "_____no_output_____" ] ], [ [ "import xtensor_monte_carlo as xmc\nimport numpy as np\nfrom bqplot import (LinearScale, Lines, Axis, Figure)...

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[ [ [ "Before running: `pip install geojsoncontour`", "_____no_output_____" ] ], [ [ "import os\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport geojsoncontour", "_____no_output_____" ], [ "# levels to draw contour lines at\nleve...

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[ [ [ "## Imports and Paths", "_____no_output_____" ], [ "# RF OOB", "_____no_output_____" ], [ "## Method", "_____no_output_____" ], [ "## Downsides", "_____no_output_____" ], [ "### Time series data", "_____no_output____...

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[ [ [ "<figure>\n <IMG SRC=\"https://raw.githubusercontent.com/pastas/pastas/master/doc/_static/Art_logo.jpg\" WIDTH=250 ALIGN=\"right\">\n</figure>\n\n# Menyanthes File\n*Developed by Ruben Caljé*", "_____no_output_____" ], [ "Menyanthes is timeseries analysis software used by many peo...

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Data Analysis Projects/Wine Qulaity Prediction/refactor_wine_quality_.ipynb

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[ [ [ "# Refactor: Wine Quality Analysis\nIn this exercise, you'll refactor code that analyzes a wine quality dataset taken from the UCI Machine Learning Repository [here](https://archive.ics.uci.edu/ml/datasets/wine+quality). Each row contains data on a wine sample, including several physicochemical proper...

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[ [ [ "Consider the population of California. If you do a Google search...you might end up at [California QuickFacts from the US Census Bureau](http://quickfacts.census.gov/qfd/states/06000.html). Compare to the [quickfacts about Alameda County](http://quickfacts.census.gov/qfd/states/06/06001.html).\n\n\nT...

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[ [ [ "## 使用TensorFlow的基本步骤\n以使用LinearRegression来预测房价为例。\n- 使用RMSE(均方根误差)评估模型预测的准确率\n- 通过调整超参数来提高模型的预测准确率", "_____no_output_____" ] ], [ [ "from __future__ import print_function\n\nimport math\n\nfrom IPython import display\nfrom matplotlib import cm\nfrom matplotlib import gridspec\...

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[ [ [ "## TensorFlow 2 Complete Project Workflow in Amazon SageMaker\n### Data Preprocessing -> Code Prototyping -> Automatic Model Tuning -> Deployment\n \n1. [Introduction](#Introduction)\n2. [SageMaker Processing for dataset transformation](#SageMakerProcessing)\n3. [Local Mode training](#LocalModeTra...

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[ [ [ "# Estimator validation\n\nThis notebook contains code to generate Figure 2 of the paper. \n\nThis notebook also serves to compare the estimates of the re-implemented scmemo with sceb package from Vasilis. \n", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport matplot...

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[ [ [ "## Find key data points from multiple documents\n\nDownload <a href=\"https://drive.google.com/file/d/1V6hmJhCqMyR65e4tal1Q70Lc_jvtZm0F/view?usp=sharing\">these documents</a>.\n\nThey all have an identical structure to them.\n\nUsing regex, capture and export as a CSV the following data points in all...

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2021-11-08T14:16:40.000Z

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2019-12-16T21:16:26.000Z

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notebooks/Automate loan approvals with business rules.ipynb

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2018-05-04T12:41:43.000Z

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notebooks/Automate loan approvals with business rules.ipynb

ODMDev/decisions-on-spark

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notebooks/Automate loan approvals with business rules.ipynb

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[ [ [ "# Automate loan approvals with Business rules in Apache Spark and Scala\n\n### Automating at scale your business decisions in Apache Spark with IBM ODM 8.9.2\n\nThis Scala notebook shows you how to execute locally business rules in DSX and Apache Spark. \nYou'll learn how to call in Apache Spark a ru...

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data-stories/happiness-data/Project Practice.ipynb

BohanMeng/storytelling-with-data

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2020-03-30T05:15:56.000Z

2022-03-21T16:24:56.000Z

data-stories/happiness-data/Project Practice.ipynb

BohanMeng/storytelling-with-data

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2019-05-03T19:34:48.000Z

2019-05-25T01:28:22.000Z

data-stories/happiness-data/Project Practice.ipynb

FanruiShao/storytelling-with-data

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[ [ [ "##World Map Plotly \n\n#Import Plotly Lib and Set up Credentials with personal account\n!pip install plotly \n\nimport plotly\n\nplotly.tools.set_credentials_file(username='igleonaitis', api_key='If6Wh3xWNmdNioPzOZZo')\nplotly.tools.set_config_file(world_readable=True,\n s...

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training/Training_edges_hog_gray.ipynb

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training/Training_edges_hog_gray.ipynb

OpenGridMap/power-grid-detection

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2018-07-22T22:43:27.000Z

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[ [ [ "from __future__ import print_function\n\nimport os\nimport sys\nimport numpy as np\n\nfrom keras.optimizers import SGD\nfrom keras.callbacks import CSVLogger, ModelCheckpoint\n\nsys.path.append(os.path.join(os.getcwd(), os.pardir))\n\nimport config\n\nfrom utils.dataset.data_generator import DataGene...

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