deep_learning_and_the_game_of_go
pycox
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| deep_learning_and_the_game_of_go | pycox | |
|---|---|---|
| 3 | 1 | |
| 929 | 753 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | 8 months ago | |
| Python | Python | |
| - | BSD 2-clause "Simplified" License |
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deep_learning_and_the_game_of_go
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Training an AI for Tigris and Euphrates
A good book I found is https://www.manning.com/books/deep-learning-and-the-game-of-go
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Why do engines often evaluate completely winning endgame positions between +60 and +63? What's significant about the low 60's as an evaluation? Or is it just a placeholder when the computer can't quite find a forced mate?
If you want to understand how the new approach used by Leela Zero and Alpha Zero works, the book Deep Learning and the Game of Go is fun and easy to read. Although it's about Go rather than chess, most of the contents are equally relevant to chess.
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[Q] Deep Learning and the Game of Go - anyone got the code to work?
One of the first hits pointed me to this github repo: https://github.com/maxpumperla/deep_learning_and_the_game_of_go
pycox
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[D] Time-varying covariates in survival analysis
take a look at https://github.com/havakv/pycox
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