Google Colaboratory Notebooks and Repositories (by @firmai)
# Unofficial Google Colaboratory Notebook and Repository Gallery

**Please contact me to take over and revamp this repo (it gets around 30k views and 200k clicks per year), I don't have time to update or maintain it - message 15/03/2021**

A curated list of repositories with fully functional click-and-run colab notebooks with data, code and description. The code in these repositories are in Python unless otherwise stated. 

To learn more about they whys and hows of Colab [see this post](https://medium.com/@firmai/google-colab-for-reproducible-research-webapps-and-data-science-fb1beec30304). For a few tips and tricks see [this post](https://www.google-colab.com/google-colab-tips-and-tricks/). 

**If you have just a single notebook to submit, use the website https://google-colab.com/, it is really easy, on the top right corner click 'submit +'. The earlier you post the more visibility you will get over time**

***Caution:*** This is a work in progress, please contribute by adding colab functionality to your own data science projects on github or requestion it from the authors.

---
 
If you want to contribute to this list (please do), send me a pull request or contact me [@dereknow](https://twitter.com/dereknow) or on [linkedin](https://www.linkedin.com/in/snowderek/).
Also, a listed repository should be fixed or removed:

* if there are no data or descriptive text in the notebooks.
* the code throws out errors.
---
* **LinkedIn**: https://www.linkedin.com/company/google-colab-notebooks/
* **Twitter**: https://twitter.com/ColabNotebooks
* **Facebook**: https://www.facebook.com/ColabNotebooks/
* **Reddit**: https://www.reddit.com/r/GoogleColabNotebooks/

---
Apart from the colab-enabled repositories listed below, you can also with a bit of work run github jupyter notebooks directly on Google Colaboratory using CPU/GPU/TPU runtimes by replacing https://github.com in the URL by https://colab.research.google.com/github/. No local installation of Python is required. Of course, these notebooks would have to be adapted to ingest the necessary data and modules.

#### Search for 'Colab' or the 'Open in Colab' Badge to Open the Colabotary Notebooks in Each Repository

## Ten Favourite Colab Notebooks

#### For more see https://google-colab.com/

* [Advanced Business Analytics and Mathematics in Python](https://github.com/firmai/business-analytics-and-mathematics-python)
* [Traffic Counting with OpenCV](https://colab.research.google.com/drive/12N4m_RYKqrpozRzh9qe7nQE_sIqQH9U8)
* [A collection of 25+ Reinforcement Learning Trading Strategies](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW)
* [Numerical Solutions for PDEs](https://colab.research.google.com/drive/1lIJ6guEAH5NQObefYBJ7S_Jm21IlJSOo)
* [Bankruptcy Prediction with Python](https://colab.research.google.com/drive/1ozRafLRWiVL9bwF5ihRUa4gz4rURKEW6)
* [Facebook Detectron2](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
* [Data Sciencing with Twitter](https://colab.research.google.com/drive/1WIcVZgbrU0DYOQqaxuaCLKY6CoLBV18O)
* [Medical Questions and Answers](https://colab.research.google.com/drive/11hAr1qo7VCSmIjWREFwyTFblU2LVeh1R)
* [BERT Movie Reviews](https://colab.research.google.com/github/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb)
* [Recurrent Neural Networks for Predictive Maintenance](https://colab.research.google.com/drive/1tjIOud2Cc6smmvZsbl-QDBA6TLA2iEtd)
* [AirBnB Sydney Rent Evaluation](https://colab.research.google.com/drive/16ILDbLl6rCD0S3r8LrEV7WXpC8LpDuo7)



# Repository Table of Contents
<!-- MarkdownTOC depth=4 -->

- [Courses and Tutorials](#course)
- [Technologies](#tech)
    - [Text](#tech-text)
    - [Image](#tech-image)
    - [Voice](#tech-voice)
    - [Reinforcement Learning](#tech-voice)
    - [Visualisation](#tech-viz)
    - [Operational](#tech-op)
    - [Other](#tech-other)
- [Applications](#app)
    - [Finance](#app-fin)
    - [Artistic](#app-art)
    - [Medical](#app-med)
    - [Operations](#app-op)

    

<a name="course"></a>
## Course and Tutorial

* [Python Data Science Notebook](https://github.com/jakevdp/PythonDataScienceHandbook) - Python Data Science Handbook: full text in Jupyter Notebooks 

* [ML and EDA](https://github.com/noahgift/functional_intro_to_python#safari-online-training--essential-machine-learning-and-exploratory-data-analysis-with-python-and-jupyter-notebook) - Functional, data science centric introduction to Python.

* [Python Business Analytics](https://github.com/firmai/python-business-analytics) - Python solutions to solve practical business problems. 

* [Deep Learning Examples](https://github.com/tugstugi/dl-colab-notebooks) - Try out deep learning models online on Google Colab 

* [Hvass-Labs](https://github.com/Hvass-Labs/TensorFlow-Tutorials) - TensorFlow Tutorials with YouTube Videos 

* [MIT deep learning](https://github.com/lexfridman/mit-deep-learning) - Tutorials, assignments, and competitions for MIT Deep Learning related courses.

* [NLP Tutorial]( https://github.com/graykode/nlp-tutorial) - Natural Language Processing Tutorial for Deep Learning Researchers 

* [DeepSchool.io](https://github.com/sachinruk/deepschool.io) - Deep Learning tutorials in jupyter notebooks. 

* [Deep NLP Course](https://github.com/DanAnastasyev/DeepNLP-Course) - A deep NLP Course 

* [pyprobml](https://github.com/probml/pyprobml) - Python code for "Machine learning: a probabilistic perspective" 

* [MIT 6.S191](https://github.com/aamini/introtodeeplearning_labs) - Lab Materials for MIT 6.S191: Introduction to Deep Learning

* [HSE NLP](https://github.com/hse-aml/natural-language-processing) - Resources for "Natural Language Processing" Coursera course

* [Real Word NLP](https://github.com/mhagiwara/realworldnlp) - Example code for "Real-World Natural Language Processing"

* [Notebooks](https://github.com/zaidalyafeai/Notebooks) - Machine learning notebooks in different subjects optimized to run in google collaboratory 




    
## Technologies
<a name="tech"></a>

<a name="tech-text"></a>
#### Text

* [BERT](https://github.com/google-research/bert) - TensorFlow code and pre-trained models for BERT 

* [XLNet](https://github.com/zihangdai/xlnet) - XLNet: Generalized Autoregressive Pretraining for Language Understanding

* [DeepPavlov Tutorials](https://github.com/deepmipt/dp_tutorials) - An open source library for deep learning end-to-end dialog systems and chatbots.

* [TF NLP](https://github.com/zhedongzheng/tensorflow-nlp) - Projects, Practice, NLP, TensorFlow 2, Google Colab 

* [SparkNLP](https://github.com/JohnSnowLabs/spark-nlp) - State of the Art Natural Language Processing 

* [Deep Text Recognition](https://github.com/clovaai/deep-text-recognition-benchmark) - Text recognition (optical character recognition) with deep learning methods.

* [BERTScore](https://github.com/Tiiiger/bert_score) - Automatic Evaluation Metric for Bert.

* [Text Summurisation](https://github.com/theamrzaki/text_summurization_abstractive_methods) - Multiple implementations for abstractive text summurization

* [GPT-2 Colab](https://github.com/ak9250/gpt-2-colab) - Retrain gpt-2 in colab 
 

<a name="tech-image"></a>
#### Image

* [DeepFaceLab](https://github.com/chervonij/DFL-Colab) - DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.

* [CycleGAN and PIX2PIX](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - Image-to-Image Translation in PyTorch 


* [DeOldify](https://github.com/jantic/DeOldify) - A Deep Learning based project for colorizing and restoring old images (and video!) 

* [Detectron2](https://github.com/facebookresearch/detectron2) - Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

* [EfficientNet - PyTorch]( https://github.com/lukemelas/EfficientNet-PyTorch) - A PyTorch implementation of EfficientNet 


* [Faceswap GAN](https://github.com/shaoanlu/faceswap-GAN) - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping. 

* [Neural Style Transfer](https://github.com/titu1994/Neural-Style-Transfer) - Keras Implementation of Neural Style Transfer from the paper "A Neural Algorithm of Artistic Style"

* [Compare GAN](https://github.com/google/compare_gan) - Compare GAN code

* [hmr](https://github.com/akanazawa/hmr) - Project page for End-to-end Recovery of Human Shape and Pose 


<a name="tech-voice"></a>
#### Voice

* [Spleeter]( https://github.com/deezer/spleeter) - Deezer source separation library including pretrained models.

* [TTS](https://github.com/mozilla/TTS) - Deep learning for Text to Speech 

<a name="tech-rl"></a>
#### Reinforcement Learning

* [Dopamine](https://github.com/google/dopamine) - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. 


* [Sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library


* [OpenSpiel](https://github.com/deepmind/open_spiel) - Collection of environments and algorithms for research in general reinforcement learning and search/planning in games. 

* [TF Agents](https://github.com/tensorflow/agents) - TF-Agents is a library for Reinforcement Learning in TensorFlow 

* [bsuite](https://github.com/deepmind/bsuite) - Collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

* [TF Generative Models](https://github.com/timsainb/tensorflow2-generative-models) - mplementations of a number of generative models in Tensorflow

 
* [DQN to Rainbow]( https://github.com/Curt-Park/rainbow-is-all-you-need) - A step-by-step tutorial from DQN to Rainbow 


<a name="tech-viz"></a>
#### Visualisation

* [Altair]( https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python

* [Altair Curriculum](https://github.com/uwdata/visualization-curriculum) - A data visualization curriculum of interactive notebooks.

* [bertviz](https://github.com/jessevig/bertviz) - Tool for visualizing attention in the Transformer model 

* [TF Graphics](https://github.com/tensorflow/graphics) - TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow 

* [deepreplay](https://github.com/dvgodoy/deepreplay) - Generate visualizations as in my "Hyper-parameters in Action!"

<a name="tech-op"></a>
#### Operational

* [PySyft](https://github.com/OpenMined/PySyft) - A library for encrypted, privacy preserving machine learning 

* [Mindsdb](https://github.com/mindsdb/mindsdb) - Framework to streamline use of neural networks

* [Ranking](https://github.com/tensorflow/ranking) - Learning to Rank in TensorFlow 

* [TensorNetwork](https://github.com/google/TensorNetwork) - A library for easy and efficient manipulation of tensor networks. 

* [JAX](https://github.com/google/jax) - Composable transformations of Python+NumPy programs

 
* [BentoML]( https://github.com/bentoml/BentoML) - A platform for serving and deploying machine learning models

<a name="tech-other"></a>
#### Other

* [Transfer learning NLP](https://github.com/huggingface/naacl_transfer_learning_tutorial) - code for the tutorial on Transfer Learning in NLP held at NAACL 2019

* [BDL Benchmarks](https://github.com/OATML/bdl-benchmarks) - Bayesian Deep Learning Benchmarks 


<a name="app"></a>
## Applications


<a name="app-fin"></a>
#### Finance

* [RLTrader](https://github.com/notadamking/RLTrader) - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

* [TF Quant Finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance. 

* [TensorTrade](https://github.com/notadamking/tensortrade) - An open source reinforcement learning framework for robust trading agents


<a name="app-art"></a>
#### Artistic

* [Rapping NN](https://github.com/robbiebarrat/rapping-neural-network) - Rap song writing recurrent neural network trained on Kanye West's entire discography 

* [Photogrammetry](https://github.com/alicevision/meshroom/wiki/Meshroom-in-Google-Colab-(cloud)) - Render Photogrammetry With Colab's Cloud GPU's With Meshroom. 

* [dl4g](https://github.com/smartgeometry-ucl/dl4g) - Deep Learning for Graphics 

<a name="app-med"></a>
#### Medical

* [DocProduct]( https://github.com/re-search/DocProduct) - Medical Q&A with Deep Language Models 

<a name="app-op"></a>
#### Operations

* [LSTM Predictive Maintenance](https://github.com/umbertogriffo/Predictive-Maintenance-using-LSTM) - Example of Multiple Multivariate Time Series Prediction with LSTM




