Some awesome AI related books and pdfs for downloading 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
Some often used Mathematic Symbols can refer this page
- OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari, Box2d, MuJoCo etc...)
- DeepMind Pysc2: StarCraft II Learning Environment.
- TorchCraftAI: A bot platform for machine learning research on StarCraft®: Brood War®
- Valve Dota2: Dota2 game acessing api. (CN doc)
- Google Dopamine: Dopamine is a research framework for fast prototyping of reinforcement learning algorithms
- TextWorld: Microsoft - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.
- Mini Grid: Minimalistic gridworld environment for OpenAI Gym
- Artificial Intelligence-A Modern Approach (3rd Edition) - Stuart Russell & peter Norvig
- 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
- Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
- 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
- Probability and Statistics 4th - Morris H. DeGroot
- 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
- 数学之美 2th - 吴军
- 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
- 离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版) - Kenneth H.Rosen
- Introduction to Data Mining - Pang-Ning Tan
- Programming Collective Intelligence - Toby Segaran
- Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
- 集体智慧编程 - Toby Segaran
- 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
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- A3C - Google DeepMind Asynchronous Advantage Actor-Critic algorithm
- Q-Learning SARSA DQN DDQN - Q-Learning is a value-based Reinforcement Learning algorithm
- DDPG - Deep Deterministic Policy Gradient,
- Large-Scale Curiosity - Large-Scale Study of Curiosity-Driven Learning
- PPO - OpenAI Proximal Policy Optimization Algorithms
- RND - OpenAI Random Network Distillation, an exploration bonus for deep reinforcement learning method.
- VIME - OpenAI Variational Information Maximizing Exploration
- DQV - Deep Quality-Value (DQV) Learning
- ERL - Evolution-Guided Policy Gradient in Reinforcement Learning
- MF Multi-Agent RL - Mean Field Multi-Agent Reinforcement Learning. (this paper include MF-Q and MF-AC)
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- scikit-feature - A collection of feature selection algorithms, available on Github
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- Xgboost (Python, R, JVM, Julia, CLI) - Xgboost lib's document.
- LightGBM (Python, R, CLI) - Microsoft lightGBM lib's features document.
- CatBoost (Python, R, CLI) - Yandex Catboost lib's key algorithm pdf papper.
- StackNet (Java, CLI) - Some model stacking algorithms implemented in this lib.
- RGF - Learning Nonlinear Functions Using
Regularized Greedy Forest(multi-core implementation FastRGF) - FM, FastFM, FFM, XDeepFM - Factorization Machines and some extended algorithms
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- GNN Papers - Must-read papers on graph neural networks (GNN)
- DenseNet - Densely Connected Convolutional Networks
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- BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding
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- Fast R-CNN - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection
- Mask R-CNN - Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
- GQN - DeepMind Generative Query Network, Neural scene representation and rendering
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- MAML - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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- GCN - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
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- TPOT (Python) - TPOT is a lib for AutoML.
- TransmogrifAI (JVM) - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark
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- t-SNE (Non-linear/Non-params) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization
- PCA (Linear) - Principal component analysis
- LDA (Linear) - Linear Discriminant Analysis
- LLE (Non-linear) - Locally linear embedding
- Laplacian Eigenmaps - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Sammon Mapping (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the two spaces