• Jupyter Notebook
• Apache Zeppelin notes
  • See example in the examples/apache-zeppelin folder
• JuPyteR notebooks
  • See example in the examples/JuPyteR folder
  • Data notebooks
• nbdime: Jupyter Notebook Diff and Merge tools - command-line tools | nbdiffex - Jupyter notebook checkpoint differ extension
• Handy python notebook having Important #ml models code in one place

• All kaggle notebooks
• Selected Kaggle notebooks
  • Hidden Gems by Martin Henze (@headsortails) - discussion topics with links to Notebooks
  • FastChai: Tweet Sentiment Extraction (analysis)
  • FastChai: Tweet Sentiment Extraction (data-prep)
  • NLP Profiler: simple dataset
  • Normalising a distribution
  • NLP Profiler Class (utility script)
  • ChaiEDA: NYC Taxi Trip Duration - analysis
  • ChaiEDA: NYC Taxi Trip Duration (data-prep)
  • ChaiEDA: India's Air Quality 2015-20 🇮🇳
  • ChaiEDA: India's Air Quality 2015-20 (data-prep)
  • ChaiEDA: Air Quality in Indian Cities 2015-20
  • ChaiEDA: Google Play Store Apps - review analysis
  • ChaiEDA: Google Play Store Apps (data-prep)
  • CTDS: answering the "what..." question differently
  • Many things Performance in Python
  • ChaiEDA sessions: Titanic - Using tools
  • ChaiEDA sessions: Titanic
  • [TSE2020] RoBERTa PyTorch multi TPU (10 skfd) 1/2
  • [TSE2020] RoBERTa PyTorch multi TPU (10 skfd) 2/2
  • [TSE2020] RoBERTa, CNN & Rnd Seed Dist. TPU
  • [TSE2020] BERTweet TPU (1/2)
  • BERT on Steroids: using PyTorch CPUs/TPUs
  • 105 Features Model - clean datasets
  • 90 Features Model - clean datasets
  • 300 Features Model - clean datasets
  • 54 Features Model - clean datasets
  • 16 Features Model (from 54 features)
  • One Feature Model - clean datasets (refactored)
  • Remove drift using a sine function (refactored)
  • Clean Removal of Data Drift (refactored)
  • Better NLP Summarisers Notebook
  • Summariser_PyTextRank_Class (utility script)
  • Summariser_TFIDF_Variation_Class (utility script)
  • Summariser_TFIDF_Class (utility script)
  • Summariser_Cosine_Class (utility script)
  • Better_NLP_Class (utility script)
  • Better NLP Class Notebook
  • Six steps to more professional data science code

See Notebooks from NVIDIA's RAPIDS

• Deep Learning with Tensorflow 2.0 (MNIST)
  • Slides
  • Notebook 1
  • Notebook 2
  • Notebook 3
  • Github for Jupiter Notebooks

• Python Data Science Handbook on Azure git repo
• Python for Data Analysis on Azure git repo
• Python Data Science Handbook
• House prices
  • ML End-to-End Tutorial Pandas notebook
  • Regression example notebook
  • Classification example notebook
  • Some explanations of the above Regression & Classification examples: as a Notebook | as a PDF file
• Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command line tools
• Synthetic features and outliers notebook
• Do we know our data...
  • Exploratory Data Analysis
  • Data Preparation
    • Data Processing
  • Feature Engineering
  • Feature Importances
    • Catboost model and W&B
    • LightGBM model and W&B

• NLP related notebooks
• Better NLP Notebooks:
  • Jupyter
    • Spacy/Texacy Examples
    • Summarisers Examples
  • Google Colab
    • Spacy/Texacy Examples
    • Summarisers Examples
• NLP Profiler Notebooks:
  • Notebooks README
  • Notebooks files
  • Notebooks/kernels from supporters
  • Kaggle Kernel
• Bag of words notebook
• TFIDF notebook

## Neural Networks

• Neural Networks Matrices exploration - Under the Hood Mathematical Operations

• Reinforcement learning notebooks: 1 | 2 | 3

• Time-series / anomaly detection notebooks

• Quick Intro to ProGAN

• ML Notebooks: hands-on
• A gallery of interesting IPython Notebooks
• Dive into Machine Learning with Jupyter notebook, Python, and scikit-learn
• "The Big Bad NLP Notebooks – Quantum Stat"

Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.

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