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
• Traffic Counting with OpenCV
• A collection of 25+ Reinforcement Learning Trading Strategies
• Numerical Solutions for PDEs
• Bankruptcy Prediction with Python
• Facebook Detectron2
• Data Sciencing with Twitter
• Medical Questions and Answers
• BERT Movie Reviews
• Recurrent Neural Networks for Predictive Maintenance
• AirBnB Sydney Rent Evaluation

Repository Table of Contents

• Courses and Tutorials
• Technologies
  • Text
  • Image
  • Voice
  • Reinforcement Learning
  • Visualisation
  • Operational
  • Other
• Applications
  • Finance
  • Artistic
  • Medical
  • Operations

Course and Tutorial

• Python Data Science Notebook - Python Data Science Handbook: full text in Jupyter Notebooks

• ML and EDA - Functional, data science centric introduction to Python.

• Python Business Analytics - Python solutions to solve practical business problems.

• Deep Learning Examples - Try out deep learning models online on Google Colab

• Hvass-Labs - TensorFlow Tutorials with YouTube Videos

• MIT deep learning - Tutorials, assignments, and competitions for MIT Deep Learning related courses.

• NLP Tutorial - Natural Language Processing Tutorial for Deep Learning Researchers

• DeepSchool.io - Deep Learning tutorials in jupyter notebooks.

• Deep NLP Course - A deep NLP Course

• pyprobml - Python code for "Machine learning: a probabilistic perspective"

• MIT 6.S191 - Lab Materials for MIT 6.S191: Introduction to Deep Learning

• HSE NLP - Resources for "Natural Language Processing" Coursera course

• Real Word NLP - Example code for "Real-World Natural Language Processing"

• Notebooks - Machine learning notebooks in different subjects optimized to run in google collaboratory

Technologies

Text

• BERT - TensorFlow code and pre-trained models for BERT

• XLNet - XLNet: Generalized Autoregressive Pretraining for Language Understanding

• DeepPavlov Tutorials - An open source library for deep learning end-to-end dialog systems and chatbots.

• TF NLP - Projects, Practice, NLP, TensorFlow 2, Google Colab

• SparkNLP - State of the Art Natural Language Processing

• Deep Text Recognition - Text recognition (optical character recognition) with deep learning methods.

• BERTScore - Automatic Evaluation Metric for Bert.

• Text Summurisation - Multiple implementations for abstractive text summurization

• GPT-2 Colab - Retrain gpt-2 in colab

Image

• DeepFaceLab - DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.

• CycleGAN and PIX2PIX - Image-to-Image Translation in PyTorch

• DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

• Detectron2 - Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

• EfficientNet - PyTorch - A PyTorch implementation of EfficientNet

• Faceswap GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

• Neural Style Transfer - Keras Implementation of Neural Style Transfer from the paper "A Neural Algorithm of Artistic Style"

• Compare GAN - Compare GAN code

• hmr - Project page for End-to-end Recovery of Human Shape and Pose

Voice

• Spleeter - Deezer source separation library including pretrained models.

• TTS - Deep learning for Text to Speech

Reinforcement Learning

• Dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

• Sonnet - TensorFlow-based neural network library

• OpenSpiel - Collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

• TF Agents - TF-Agents is a library for Reinforcement Learning in TensorFlow

• bsuite - Collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

• TF Generative Models - mplementations of a number of generative models in Tensorflow

• DQN to Rainbow - A step-by-step tutorial from DQN to Rainbow

Visualisation

• Altair - Declarative statistical visualization library for Python

• Altair Curriculum - A data visualization curriculum of interactive notebooks.

• bertviz - Tool for visualizing attention in the Transformer model

• TF Graphics - TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow

• deepreplay - Generate visualizations as in my "Hyper-parameters in Action!"

Operational

• PySyft - A library for encrypted, privacy preserving machine learning

• Mindsdb - Framework to streamline use of neural networks

• Ranking - Learning to Rank in TensorFlow

• TensorNetwork - A library for easy and efficient manipulation of tensor networks.

• JAX - Composable transformations of Python+NumPy programs

• BentoML - A platform for serving and deploying machine learning models

Other

• Transfer learning NLP - code for the tutorial on Transfer Learning in NLP held at NAACL 2019

• BDL Benchmarks - Bayesian Deep Learning Benchmarks

Applications

Finance

• RLTrader - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

• TF Quant Finance - High-performance TensorFlow library for quantitative finance.

• TensorTrade - An open source reinforcement learning framework for robust trading agents

Artistic

• Rapping NN - Rap song writing recurrent neural network trained on Kanye West's entire discography

• Photogrammetry - Render Photogrammetry With Colab's Cloud GPU's With Meshroom.

• dl4g - Deep Learning for Graphics

Medical

• DocProduct - Medical Q&A with Deep Language Models

Operations

• LSTM Predictive Maintenance - Example of Multiple Multivariate Time Series Prediction with LSTM

原文：https://github.com/firmai/awesome-google-colab