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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code and other material for the book "Deep Learning and the Game of Go"
| Date | Stars |
|---|---|
| 2026-07-31 | 1057 |
| 2026-08-06 | 1057 |
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# Deep Learning and the Game of Go [](https://travis-ci.org/maxpumperla/deep_learning_and_the_game_of_go) This repository is first and foremost a comprehensive machine learning framework for the game of Go, focussing on deep learning techniques. What you'll find here is a library that builds up from the game-play basics to very advanced techniques. In particular, you find code **for early approaches in game AI, intermediate techniques using deep learning, to implementations of AlphaGo and AlphaGo Zero - all presented in one common framework**. You can install this library with pip and follow the `examples` in the `code` folder. ```bash pip install dlgo ``` On the other hand, this repository at the same time contains [Code](https://github.com/maxpumperla/deep_learning_and_the_game_of_go/tree/master/code), and sample chapters for the book "Deep Learning and the Game of Go" (Manning), available for early access [here](https://www.manning.com/books/deep-learning-and-the-game-of-go), which ties into the library and teaches its components bit by biy. If you're following the code samples from the book, check out the branches for individual chapters. *Note for contributors*: To ensure the book stays in sync, consider requesting changes and submitting pull requests against the `improvements` branch, instead of `master` (which we keep reserved for bug fixes etc.).  ## Playable demos The book is all about getting you started to create your own bots. To make the experience more fun and interactive, we built and deployed several bots showcasing the techniques of the respective chapter. So far you can play: - **Chapter 4** A tiny tree search bot on a 5x5 board [here](https://demos.badukai.com/static/play_mcts_55.html). - **Chapter 7** A full 19x19 bot powered by a deep neural network trained to predict human moves [here](https://demos.badukai.com/static/play_predict_19.html) - **Chapter 9** A bot playing on a 9x9 board, using policy gradients, [here](https://demos.badukai.com/static/play_pg_99.html) These demos will be available in the [liveBook](https://www.manning.com/books/deep-learning-and-the-game-of-go) version of the book as well. <iframe src="https://www.badukai.com/demos/static/play_predict_19.html" height="500" width="800" style="border:2px solid grey; background-color: #f8f8f8;"></iframe> ## Table of Contents 1. Toward deep learning: a machine learning introduction 2. Go as a machine learning problem 3. Implementing your first Go bot 4. Playing games with tree search 5. Getting started with neural networks 6. Designing a neural network for Go data 7. Learning from data: a deep learning bot 8. Deploying bots in the wild 9. Enter deep reinforcement learning 10. Reinforcement learning with policy gradients 11. Reinforcement learning with value methods 12. Reinforcement learning with actor-critic methods 13. AlphaGo: Combining approaches 14. AlphaGoZero and AlphaZero: Combining approaches Appendices - A. Mathematical foundations with Python - B. The backpropagation algorithm - C. Go programs and servers - D. Training and deploying bots using Amazon Web Services - E. Submitting a bot to the Online Go Server (OGS)  ## Welcome When AlphaGo hit the news in early 2016, we were extremely excited about this groundbreaking advancement in computer Go. At the time, it was largely conjectured that human-level artificial intelligence for the game of Go was at least 10 years in the future. We followed the games meticulously and didn’t shy away from waking up early or staying up late to watch the broadcasted games live. Indeed, we had good company — millions of people around the globe were captivated by the games against Fan Hui, Lee Sedol, and later, Ke Jie and others. Shortly after the emergence of AlphaGo, we picked up work on a little open source libra
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Max Pumperla · Manyfold Labs
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Tycho Tatitscheff · Gadz.org · France
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:71be135fc8cf5fb4, topic:deep-learning