Some awesome AI related books and pdfs for download and learning.
This repo only used for learning, do not use in business, and welcome for providing great books in this repo, if you have great books for sharing, please create PR for me, if any books have new edition, also welcome to update it.
Due to github Large file storage limition issue, all books pdf stored in gitlab repo, please also create PR using git-lfs for gitlab repo
- OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari, Box2d, MuJoCo etc...)
- DeepMind Pysc2: StarCraft II Learning Environment.
- Valve Dota2: Dota2 game acessing api. (CN doc)
- Google Dopamine: Dopamine is a research framework for fast prototyping of reinforcement learning algorithms
- Introduction to Data Mining - Pang-Ning Tan
- Programming Collective Intelligence - Toby Segaran
- 集体智慧编程 - Toby Segaran
- A First Course in ProbabilityA First Course in Probability (8th) - Sheldon M Ross
- Convex Optimization - Stephen Boyd
- Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
- Introduction to Linear Algebra (5th) - Gilbert Strang
- Linear Algebra and Its Applications (5th) - David C Lay
- Probability Theory The Logic of Science - Edwin Thompson Jaynes
- Statistical Inference (2nd) - Roger Casella
- 信息论基础 (原书Elements of Information Theory Elements第2版) - Thomas Cover & Jay A Thomas
- 凸优化 (原书Convex Optimization) - Stephen Boyd
- 数理统计学教程 - 陈希儒
- 概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版) - Sheldon M Ross
- 线性代数及其应用 (原书Linear Algebra and Its Applications第3版) - David C Lay
- 统计推断 (原书Statistical Inference第二版) - Roger Casella
- Information Theory, Inference and Learning Algorithms - David J C MacKay
- Machine Learning - Tom M. Mitchell
- Pattern Recognition and Machine Learning - Christopher Bishop
- The Elements of Statistical Learning - Trevor Hastie
- 机器学习 - 周志华
- 机器学习 (原书Machine Learning) - Tom M. Mitchell
- 统计学习方法 - 李航
- Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
- Deep Learning Methods and Applications - Li Deng & Dong Yu
- Learning Deep Architectures for AI - Yoshua Bengio
- Machine Learning An Algorithmic Perspective (2nd) - Stephen Marsland
- Neural Network Design (2nd) - Martin Hagan
- Neural Networks and Learning Machines (3rd) - Simon Haykin
- Neural Networks for Applied Sciences and Engineering - Sandhya Samarasinghe
- 深度学习 (原书Deep Learning) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
- 神经网络与机器学习 (原书Neural Networks and Learning Machines) - Simon Haykin
- 神经网络设计 (原书Neural Network Design) - Martin Hagan
- Machine Learning
- Xgboost - Xgboost lib's document.
- LightGBM - Microsoft lightGBM lib's features document.
- CatBoost - Yandex Catboost lib's key algorithm pdf papper.
- StackNet - Some model stacking algorithms implemented in this lib.
- RGF - Learning Nonlinear Functions Using
Regularized Greedy Forest(multi-core implementation FastRGF) - FM, FastFM, FFM - Factorization Machines and some extended algorithms
- Auto ML
- TPOT - TPOT is a lib for AutoML.
