deep_learning_and_the_game_of_go
deepxde
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| deep_learning_and_the_game_of_go | deepxde | |
|---|---|---|
| 3 | 2 | |
| 929 | 2,343 | |
| - | - | |
| 0.0 | 8.6 | |
| over 1 year ago | 10 days ago | |
| Python | Python | |
| - | GNU Lesser General Public License v3.0 only |
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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
deepxde
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[Dev-Showcase] Airfoil Optimisation using Physics Informed Neural Networks(PINNs)
Due to certain limitations in MODULUS(We are unable to directly access the point cloud), we are now also exploring other available PINNs libraries and frameworks and stumbled on to deepXDE. deepXDE is a little different than Modulus and I'm currently exploring it.
- Physics-Informed ML Simulator for Wildfire Propagation (Video)
What are some alternatives?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
GeneticAlgorithmPython - Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
pycox - Survival analysis with PyTorch
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Keras - Deep Learning for humans
pymadcad - Simple yet powerful CAD (Computer Aided Design) library, written with Python.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)