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 Awesome AI books
 Awesome AI books
Some awesome AI related books and pdfs for downloading and learning.
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Training ground
- OpenAI Gym (https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari (https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo 
etc...)
- OpenAI Gym (https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari (https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo etc...)
- malmo (https://github.com/Microsoft/malmo): Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. 
- DeepMind Pysc2 (https://github.com/deepmind/pysc2): StarCraft II Learning Environment.
- Procgen (https://github.com/openai/procgen): Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments.
@@ -67,8 +66,7 @@
- gym-sokoban (https://github.com/mpSchrader/gym-sokoban): Sokoban is Japanese for warehouse keeper and a traditional video game
- Pommerman (https://github.com/MultiAgentLearning/playground) Playground hosts Pommerman, a clone of Bomberman built for AI research.
- gym-miniworld (https://github.com/maximecb/gym-miniworld#introduction) MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research
- vizdoomgym (https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for ViZDoom (https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from 
Raw Visual Information) enviroments.
- vizdoomgym (https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for ViZDoom (https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments.
- ddz-ai (https://github.com/freefuiiismyname/ddz-ai) 以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai
@@ -112,8 +110,7 @@
- Machine Learning (https://yadi.sk/i/03Jg9WMzgD2YlA) - Tom M. Mitchell
- Pattern Recognition and Machine Learning (https://yadi.sk/i/8ffTCaMH0bM8uQ) - Christopher Bishop
- The Elements of Statistical Learning (https://yadi.sk/i/hfatiRyBCwfcWw) - Trevor Hastie
- Machine Learning for OpenCV (https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler (Source code here 
(https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
- Machine Learning for OpenCV (https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler (Source code here (https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
- 机器学习 (https://yadi.sk/i/vfoPTRRfgtEQKA) - 周志华
- 机器学习 (原书Machine Learning) (https://yadi.sk/i/jTNv4kzG-lmlYQ) - Tom M. Mitchell
- 统计学习方法 (https://yadi.sk/i/R08dbDMOJb3KKw) - 李航
@@ -137,12 +134,11 @@
- COMMERCIAL Conversational AI (https://www.manning.com/books/conversational-ai) - Andrew R. Freed
Philosophy
- COMMERCIAL Human Compatible: Artificial Intelligence and the Problem of Control (https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart 
Russell
- COMMERCIAL Human Compatible: Artificial Intelligence and the Problem of Control (https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart Russell
- COMMERCIAL Life 3.0: Being Human in the Age of Artificial Intelligence (https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598) - Max Tegmark
- COMMERCIAL Superintelligence: Paths, Dangers, Strategies 
(https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=pd_sbs_14_t_0/146-0357100-6717505?_encoding=UTF8&pd_rd_i=0198739834&pd_rd_r=676ace91-552c-4865-a8d3
-6273db5418bf&pd_rd_w=zYEu2&pd_rd_wg=hQdGQ&pf_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ) - Nick Bostrom
(https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=pd_sbs_14_t_0/146-0357100-6717505?_encoding=UTF8&pd_rd_i=0198739834&pd_rd_r=676ace91-552c-4865-a8d3-6273db5418bf&pd_rd_w=zYEu2&pd_rd_wg=hQdGQ&pf
_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ) - Nick Bostrom
Quantum with AI
@@ -164,14 +160,13 @@
 - Stable Diffusion (https://github.com/CompVis/stable-diffusion) - Paper (https://arxiv.org/abs/2112.10752) A latent text-to-image diffusion model
 - Stable Diffusion V2 (https://github.com/Stability-AI/stablediffusion) - High-Resolution Image Synthesis with Latent Diffusion Models
 - GFPGAN (https://github.com/TencentARC/GFPGAN) - Paper (https://arxiv.org/abs/2101.04061) GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
 - ESRGAN (https://github.com/xinntao/ESRGAN) - Paper (https://arxiv.org/abs/2107.10833) ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The 
training codes are in BasicSR.
 - ESRGAN (https://github.com/xinntao/ESRGAN) - Paper (https://arxiv.org/abs/2107.10833) ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
 - CodeFormer (https://github.com/sczhou/CodeFormer) - Paper (https://arxiv.org/abs/2206.11253) - NeurIPS 2022 Towards Robust Blind Face Restoration with Codebook Lookup Transformer
 - UniPC (https://github.com/wl-zhao/UniPC) - Paper (https://arxiv.org/abs/2302.04867) UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
- #### Reinforcement Learning
 - A3C (https://arxiv.org/pdf/1602.01783.pdf) - Google DeepMind Asynchronous Advantage Actor-Critic algorithm
 - Q-Learning (http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf) SARSA DQN (https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) DDQN 
(https://arxiv.org/pdf/1509.06461.pdf) - Q-Learning is a value-based Reinforcement Learning algorithm
 - Q-Learning (http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf) SARSA DQN (https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) DDQN (https://arxiv.org/pdf/1509.06461.pdf) - Q-Learning is a value-based Reinforcement
Learning algorithm
 - DDPG (https://arxiv.org/pdf/1509.02971.pdf) - Deep Deterministic Policy Gradient,
 - Large-Scale Curiosity (https://arxiv.org/pdf/1808.04355.pdf) - Large-Scale Study of Curiosity-Driven Learning
 - PPO (https://arxiv.org/pdf/1707.06347.pdf) - OpenAI Proximal Policy Optimization Algorithms
@@ -190,10 +185,9 @@
 - LightGBM (https://lightgbm.readthedocs.io/en/latest/Features.html#) (Python, R, CLI) - Microsoft lightGBM lib's features document.
 - CatBoost (https://arxiv.org/pdf/1706.09516.pdf) (Python, R, CLI) - Yandex Catboost lib's key algorithm pdf papper.
 - StackNet (https://github.com/kaz-Anova/StackNet) (Java, CLI) - Some model stacking algorithms implemented in this lib.
 - RGF (https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using Regularized Greedy Forest (multi-core implementation FastRGF 
(https://github.com/RGF-team/rgf/tree/master/FastRGF))
 - FM (https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), FastFM (https://arxiv.org/pdf/1505.00641.pdf), FFM (https://arxiv.org/pdf/1701.04099.pdf), XDeepFM 
(https://arxiv.org/pdf/1803.05170.pdf) - Factorization Machines and some extended Algorithms
 - RGF (https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using Regularized Greedy Forest (multi-core implementation FastRGF (https://github.com/RGF-team/rgf/tree/master/FastRGF))
 - FM (https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), FastFM (https://arxiv.org/pdf/1505.00641.pdf), FFM (https://arxiv.org/pdf/1701.04099.pdf), XDeepFM (https://arxiv.org/pdf/1803.05170.pdf) - Factorization Machines and 
some extended Algorithms
- #### Deep Learning
 - GNN Papers (https://github.com/thunlp/GNNPapers) - Must-read papers on graph neural networks (GNN)
 - EfficientNet (https://arxiv.org/pdf/1905.11946.pdf) - Rethinking Model Scaling for Convolutional Neural Networks
@@ -204,8 +198,7 @@
 - GPT-3 (https://arxiv.org/pdf/2005.14165.pdf) - Language Models are Few-Shot Learners
- #### CV
 - Fast R-CNN (https://arxiv.org/pdf/1504.08083.pdf) - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection
 - Mask R-CNN (https://arxiv.org/pdf/1703.06870.pdf) - 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.
 - Mask R-CNN (https://arxiv.org/pdf/1703.06870.pdf) - 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 (http://science.sciencemag.org/content/360/6394/1204/tab-pdf) - DeepMind Generative Query Network, Neural scene representation and rendering
- #### Meta Learning
 - MAML (https://arxiv.org/pdf/1703.03400.pdf) - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
@@ -227,16 +220,14 @@
 - LDA (https://www.isip.piconepress.com/publications/reports/1998/isip/lda/lda_theory.pdf) (Linear) - Linear Discriminant Analysis
 - LLE (https://cs.nyu.edu/~roweis/lle/papers/lleintro.pdf) (Non-linear) - Locally linear embedding
 - Laplacian Eigenmaps (http://web.cse.ohio-state.edu/~belkin.8/papers/LEM_NC_03.pdf) - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
 - Sammon Mapping (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding 
inter-point distances in the
 - Sammon Mapping (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the
two spaces
- #### Data Processing
 - Pandas (https://github.com/pandas-dev/pandas) (Python) - Flexible and powerful data analysis / manipulation library for Python.
 - Polars (https://github.com/pola-rs/polars) (Rust, Python) - Lightning-fast DataFrame library for Rust and Python.
Distributed training
- Horovod (https://github.com/horovod/horovod#usage) - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep 
Learning fast and easy to use.
- Horovod (https://github.com/horovod/horovod#usage) - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and easy to use.
- Acme (https://github.com/deepmind/acme) - A Research Framework for (Distributed) Reinforcement Learning. 
- bagua (https://github.com/BaguaSys/bagua) - Bagua is a flexible and performant distributed training algorithm development framework.