[38;5;12m                                                                                                              [39m[38;2;255;187;0m[1m[4mAwesome AI books[0m

[38;5;12mSome awesome AI related books and pdfs for downloading and learning.[39m

[38;2;255;187;0m[4mPreface[0m

[38;5;14m[1mThis repo only used for learning, do not use in business.[0m

[38;5;12mWelcome for providing great books in this repo or tell me which great book you need and I will try to append it in this repo, any idea you can create issue or PR here.[39m

[38;5;12mDue to github Large file storage limition, all books pdf stored in [39m[38;5;14m[1mYandex.Disk[0m[38;5;12m.[39m

[38;5;12mSome often used [39m[38;5;14m[1mMathematic Symbols[0m[38;5;12m can refer this [39m[38;5;14m[1mpage[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books/blob/master/math-symbols.md)[39m

[38;2;255;187;0m[4mContent[0m
[38;5;12m- [39m[38;5;14m[1mOrganization with papers/researchs[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#organization-with-papersresearchs)[39m
[38;5;12m- [39m[38;5;14m[1mTraining ground[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#training-ground)[39m
[38;5;12m- [39m[38;5;14m[1mBooks[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#books)[39m
[38;5;12m  - [39m[38;5;14m[1mIntroductory theory and get start[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#introductory-theory-and-get-start)[39m
[38;5;12m  - [39m[38;5;14m[1mMathematics[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#mathematics)[39m
[38;5;12m  - [39m[38;5;14m[1mData mining[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#data-mining)[39m
[38;5;12m  - [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#deep-learning)[39m
[38;5;12m  - [39m[38;5;14m[1mPhilosophy[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#philosophy)[39m
[38;5;12m- [39m[38;5;14m[1mQuantum with AI[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#quantum-with-ai)[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum Basic[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#quantum-basic)[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum AI[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#quantum-ai)[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum Related Framework[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#quantum-related-framework)[39m
[38;5;12m- [39m[38;5;14m[1mLibs With Online Books[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#libs-with-online-books)[39m
[38;5;12m  - [39m[38;5;14m[1mReinforcement Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#reinforcement-learning)[39m
[38;5;12m  - [39m[38;5;14m[1mFeature Selection[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#feature-selection)[39m
[38;5;12m  - [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#machine-learning-1)[39m
[38;5;12m  - [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#deep-learning-1)[39m
[38;5;12m  - [39m[38;5;14m[1mNLP[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#nlp)[39m
[38;5;12m  - [39m[38;5;14m[1mCV[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#cv)[39m
[38;5;12m  - [39m[38;5;14m[1mMeta Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#meta-learning)[39m
[38;5;12m  - [39m[38;5;14m[1mTransfer Learning[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#transfer-learning)[39m
[38;5;12m  - [39m[38;5;14m[1mAuto ML[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#auto-ml)[39m
[38;5;12m  - [39m[38;5;14m[1mDimensionality Reduction[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#dimensionality-reduction)[39m
[38;5;12m- [39m[38;5;14m[1mDistributed training[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books#distributed-training)[39m

[38;2;255;187;0m[4mOrganization with papers/researchs[0m

[38;5;12m- [39m[38;5;14m[1marxiv.org[0m[38;5;12m (https://arxiv.org/)[39m
[38;5;12m- [39m[38;5;14m[1mScience[0m[38;5;12m (http://www.sciencemag.org/)[39m
[38;5;12m- [39m[38;5;14m[1mNature[0m[38;5;12m (https://www.nature.com/nature/)[39m
[38;5;12m- [39m[38;5;14m[1mDeepMind Publications[0m[38;5;12m (https://deepmind.com/research/publications/)[39m
[38;5;12m- [39m[38;5;14m[1mOpenAI Research[0m[38;5;12m (https://openai.com/research/)[39m

[38;2;255;187;0m[4mTraining ground[0m

[38;5;12m- [39m[38;5;14m[1mOpenAI Gym[0m[38;5;12m (https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with [39m[38;5;14m[1mAtari[0m[38;5;12m (https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo etc...)[39m
[38;5;12m- [39m[38;5;14m[1mmalmo[0m[38;5;12m (https://github.com/Microsoft/malmo): Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. [39m
[38;5;12m- [39m[38;5;14m[1mDeepMind Pysc2[0m[38;5;12m (https://github.com/deepmind/pysc2): StarCraft II Learning Environment.[39m
[38;5;12m- [39m[38;5;14m[1mProcgen[0m[38;5;12m (https://github.com/openai/procgen): Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments.[39m
[38;5;12m- [39m[38;5;14m[1mTorchCraftAI[0m[38;5;12m (https://torchcraft.github.io/TorchCraftAI/): A bot platform for machine learning research on StarCraft®: Brood War®[39m
[38;5;12m- [39m[38;5;14m[1mValve Dota2[0m[38;5;12m (https://developer.valvesoftware.com/wiki/Dota_Bot_Scripting): Dota2 game acessing api. ([39m[38;5;14m[1mCN doc[0m[38;5;12m (https://developer.valvesoftware.com/wiki/Dota_Bot_Scripting:zh-cn))[39m
[38;5;12m- [39m[38;5;14m[1mMario AI Framework[0m[38;5;12m (https://github.com/amidos2006/Mario-AI-Framework): A Mario AI framework for using AI methods.[39m
[38;5;12m- [39m[38;5;14m[1mGoogle Dopamine[0m[38;5;12m (https://github.com/google/dopamine): Dopamine is a research framework for fast prototyping of reinforcement learning algorithms[39m
[38;5;12m- [39m[38;5;14m[1mTextWorld[0m[38;5;12m (https://github.com/Microsoft/TextWorld): Microsoft - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.[39m
[38;5;12m- [39m[38;5;14m[1mMini Grid[0m[38;5;12m (https://github.com/maximecb/gym-minigrid): Minimalistic gridworld environment for OpenAI Gym[39m
[38;5;12m- [39m[38;5;14m[1mMAgent[0m[38;5;12m (https://github.com/geek-ai/MAgent): A Platform for Many-agent Reinforcement Learning[39m
[38;5;12m- [39m[38;5;14m[1mXWorld[0m[38;5;12m (https://github.com/PaddlePaddle/XWorld): A C++/Python simulator package for reinforcement learning[39m
[38;5;12m- [39m[38;5;14m[1mNeural MMO[0m[38;5;12m (https://github.com/openai/neural-mmo): A Massively Multiagent Game Environment[39m
[38;5;12m- [39m[38;5;14m[1mMinAtar[0m[38;5;12m (https://github.com/kenjyoung/MinAtar): MinAtar is a testbed for AI agents which implements miniaturized version of several Atari 2600 games.[39m
[38;5;12m- [39m[38;5;14m[1mcraft-env[0m[38;5;12m (https://github.com/Feryal/craft-env): CraftEnv is a 2D crafting environment[39m
[38;5;12m- [39m[38;5;14m[1mgym-sokoban[0m[38;5;12m (https://github.com/mpSchrader/gym-sokoban): Sokoban is Japanese for warehouse keeper and a traditional video game[39m
[38;5;12m- [39m[38;5;14m[1mPommerman[0m[38;5;12m (https://github.com/MultiAgentLearning/playground) Playground hosts Pommerman, a clone of Bomberman built for AI research.[39m
[38;5;12m- [39m[38;5;14m[1mgym-miniworld[0m[38;5;12m (https://github.com/maximecb/gym-miniworld#introduction) MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research[39m
[38;5;12m- [39m[38;5;14m[1mvizdoomgym[0m[38;5;12m (https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for [39m[38;5;14m[1mViZDoom[0m[38;5;12m (https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments.[39m
[38;5;12m- [39m[38;5;14m[1mddz-ai[0m[38;5;12m (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[39m


[38;2;255;187;0m[4mBooks[0m

[38;2;255;187;0m[4mIntroductory theory and get start[0m

[38;5;12m- [39m[38;5;14m[1mArtificial Intelligence-A Modern Approach (3rd Edition)[0m[38;5;12m (https://yadi.sk/i/G6NlUUV8SAVimg) - Stuart Russell & peter Norvig[39m
[38;5;12m- [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mGrokking Artificial Intelligence Algorithms[0m[38;5;12m (https://www.manning.com/books/grokking-artificial-intelligence-algorithms) - Rishal Hurbans[39m

[38;2;255;187;0m[4mMathematics[0m

[38;5;12m- [39m[38;5;14m[1mA First Course in ProbabilityA First Course in Probability (8th)[0m[38;5;12m (https://yadi.sk/i/aDvGdqWlcXxbhQ) - Sheldon M Ross[39m
[38;5;12m- [39m[38;5;14m[1mConvex Optimization[0m[38;5;12m (https://yadi.sk/i/9KGVXuFJs3kakg) - Stephen Boyd[39m
[38;5;12m- [39m[38;5;14m[1mElements of Information Theory Elements[0m[38;5;12m (https://yadi.sk/i/2YWnNsAeBc9qcA) - Thomas Cover & Jay A Thomas[39m
[38;5;12m- [39m[38;5;14m[1mDiscrete Mathematics and Its Applications 7th[0m[38;5;12m (https://yadi.sk/i/-r3jD4gB-8jn1A) - Kenneth H. Rosen[39m
[38;5;12m- [39m[38;5;14m[1mIntroduction to Linear Algebra (5th)[0m[38;5;12m (http://www.mediafire.com/file/f31dl0ghup7e6gk/Introduction_to_Linear_Algebra_5th_-_Gilbert_Strang.pdf/file) - Gilbert Strang[39m
[38;5;12m- [39m[38;5;14m[1mLinear Algebra and Its Applications (5th)[0m[38;5;12m (https://yadi.sk/i/uWEQVrCquqw1Ug) - David C Lay[39m
[38;5;12m- [39m[38;5;14m[1mProbability Theory The Logic of Science[0m[38;5;12m (https://yadi.sk/i/TKQYNPSKGNbdUw) - Edwin Thompson Jaynes[39m
[38;5;12m- [39m[38;5;14m[1mProbability and Statistics 4th[0m[38;5;12m (https://yadi.sk/i/38jrMmEXnJQZqg) - Morris H. DeGroot[39m
[38;5;12m- [39m[38;5;14m[1mStatistical Inference (2nd)[0m[38;5;12m (https://yadi.sk/i/HWrbKYrYdpNMYw) - Roger Casella[39m
[38;5;12m- [39m[38;5;14m[1m信息论基础 (原书Elements of Information Theory Elements第2版)[0m[38;5;12m (https://yadi.sk/i/HqGOyAkRCxCwIQ) - Thomas Cover & Jay A Thomas[39m
[38;5;12m- [39m[38;5;14m[1m凸优化 (原书Convex Optimization)[0m[38;5;12m (https://yadi.sk/i/zUPPAi58v1gfkw) - Stephen Boyd[39m
[38;5;12m- [39m[38;5;14m[1m数理统计学教程[0m[38;5;12m (https://yadi.sk/i/ikuXCrNgRCEVnw) - 陈希儒[39m
[38;5;12m- [39m[38;5;14m[1m数学之美 2th[0m[38;5;12m (https://yadi.sk/i/QJPxzK4ZBuF8iQ) - 吴军[39m
[38;5;12m- [39m[38;5;14m[1m概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版)[0m[38;5;12m (https://yadi.sk/i/wQZQ80UFLFZ48w) - Sheldon M Ross[39m
[38;5;12m- [39m[38;5;14m[1m线性代数及其应用 (原书Linear Algebra and Its Applications第3版)[0m[38;5;12m (https://yadi.sk/i/cNNBS4eaLleR3g) - David C Lay[39m
[38;5;12m- [39m[38;5;14m[1m统计推断 (原书Statistical Inference第二版)[0m[38;5;12m (https://yadi.sk/i/ksHAFRUSaoyk9g) - Roger Casella[39m
[38;5;12m- [39m[38;5;14m[1m离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版)[0m[38;5;12m (https://yadi.sk/i/kJHMmMA4ot66bw) - Kenneth H.Rosen[39m

[38;2;255;187;0m[4mData mining[0m

[38;5;12m- [39m[38;5;14m[1mIntroduction to Data Mining[0m[38;5;12m (https://yadi.sk/i/H7wc_FaMDl9QXQ) - Pang-Ning Tan[39m
[38;5;12m- [39m[38;5;14m[1mProgramming Collective Intelligence[0m[38;5;12m (https://yadi.sk/i/YTjrJWu7kXVrGQ) - Toby Segaran[39m
[38;5;12m- [39m[38;5;14m[1mFeature Engineering for Machine Learning[0m[38;5;12m (https://yadi.sk/i/WiO7lageMIuIfg) - Amanda Casari, Alice Zheng[39m
[38;5;12m- [39m[38;5;14m[1m集体智慧编程[0m[38;5;12m (https://yadi.sk/i/0DW5reTrXQ6peQ) - Toby Segaran[39m

[38;2;255;187;0m[4mMachine Learning[0m

[38;5;12m- [39m[38;5;14m[1mInformation Theory, Inference and Learning Algorithms[0m[38;5;12m (https://yadi.sk/i/JXYto8yE6PJO8Q) - David J C MacKay[39m
[38;5;12m- [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (https://yadi.sk/i/03Jg9WMzgD2YlA) - Tom M. Mitchell[39m
[38;5;12m- [39m[38;5;14m[1mPattern Recognition and Machine Learning[0m[38;5;12m (https://yadi.sk/i/8ffTCaMH0bM8uQ) - Christopher Bishop[39m
[38;5;12m- [39m[38;5;14m[1mThe Elements of Statistical Learning[0m[38;5;12m (https://yadi.sk/i/hfatiRyBCwfcWw) - Trevor Hastie[39m
[38;5;12m- [39m[38;5;14m[1mMachine Learning for OpenCV[0m[38;5;12m (https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler ([39m[38;5;14m[1mSource code here[0m[38;5;12m (https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))[39m
[38;5;12m- [39m[38;5;14m[1m机器学习[0m[38;5;12m (https://yadi.sk/i/vfoPTRRfgtEQKA) - 周志华[39m
[38;5;12m- [39m[38;5;14m[1m机器学习 (原书Machine Learning)[0m[38;5;12m (https://yadi.sk/i/jTNv4kzG-lmlYQ) - Tom M. Mitchell[39m
[38;5;12m- [39m[38;5;14m[1m统计学习方法[0m[38;5;12m (https://yadi.sk/i/R08dbDMOJb3KKw) - 李航[39m

[38;2;255;187;0m[4mDeep Learning[0m
[38;5;12m- Online Quick learning[39m
[38;5;12m  - [39m[38;5;14m[1mDive into Deep Learning[0m[38;5;12m (https://d2l.ai/) - (Using MXNet)An interactive deep learning book with code, math, and discussions.[39m
[38;5;12m  - [39m[38;5;14m[1md2l-pytorch[0m[38;5;12m (https://github.com/dsgiitr/d2l-pytorch) - (Dive into Deep Learning) pytorch version.[39m
[38;5;12m  - [39m[38;5;14m[1m动手学深度学习[0m[38;5;12m (https://zh.d2l.ai/) - (Dive into Deep Learning) for chinese.[39m
[38;5;12m- [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (https://yadi.sk/i/2fOK_Xib-JlocQ) - Ian Goodfellow & Yoshua Bengio & Aaron Courville[39m
[38;5;12m- [39m[38;5;14m[1mDeep Learning Methods and Applications[0m[38;5;12m (https://yadi.sk/i/uQAWfeKVmenmkg) - Li Deng & Dong Yu[39m
[38;5;12m- [39m[38;5;14m[1mLearning Deep Architectures for AI[0m[38;5;12m (https://yadi.sk/i/AWpRq2hSB9RmoQ) - Yoshua Bengio[39m
[38;5;12m- [39m[38;5;14m[1mMachine Learning An Algorithmic Perspective (2nd)[0m[38;5;12m (https://yadi.sk/i/1gOQ-Y5r4uP6Kw) - Stephen Marsland[39m
[38;5;12m- [39m[38;5;14m[1mNeural Network Design (2nd)[0m[38;5;12m (https://yadi.sk/i/5LLMPfNcuaPTvQ) - Martin Hagan[39m
[38;5;12m- [39m[38;5;14m[1mNeural Networks and Learning Machines (3rd)[0m[38;5;12m (https://yadi.sk/i/6s9AauRP1OGT2Q) - Simon Haykin[39m
[38;5;12m- [39m[38;5;14m[1mNeural Networks for Applied Sciences and Engineering[0m[38;5;12m (https://yadi.sk/i/JK7aj5TsmoC1dA) - Sandhya Samarasinghe[39m
[38;5;12m- [39m[38;5;14m[1m深度学习 (原书Deep Learning)[0m[38;5;12m (https://yadi.sk/i/DzzZU_QPosSTBQ) - Ian Goodfellow & Yoshua Bengio & Aaron Courville[39m
[38;5;12m- [39m[38;5;14m[1m神经网络与机器学习 (原书Neural Networks and Learning Machines)[0m[38;5;12m (https://yadi.sk/i/ogQff9JpLEdHMg) - Simon Haykin[39m
[38;5;12m- [39m[38;5;14m[1m神经网络设计 (原书Neural Network Design)[0m[38;5;12m (https://yadi.sk/i/uR2OAHHgnZHUuw) - Martin Hagan[39m
[38;5;12m- [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mInterpretable AI[0m[38;5;12m (https://www.manning.com/books/interpretable-ai) - Ajay Thampi[39m
[38;5;12m- [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mConversational AI[0m[38;5;12m (https://www.manning.com/books/conversational-ai) - Andrew R. Freed[39m

[38;2;255;187;0m[4mPhilosophy[0m
[38;5;12m- [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mHuman Compatible: Artificial Intelligence and the Problem of Control[0m[38;5;12m (https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart Russell[39m
[38;5;12m- [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mLife 3.0: Being Human in the Age of Artificial Intelligence[0m[38;5;12m (https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598) - Max Tegmark[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mCOMMERCIAL[0m[38;5;12m [39m[38;5;14m[1mSuperintelligence:[0m[38;5;14m[1m [0m[38;5;14m[1mPaths,[0m[38;5;14m[1m [0m[38;5;14m[1mDangers,[0m[38;5;14m[1m [0m[38;5;14m[1mStrategies[0m[38;5;12m [39m
[38;5;12m(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[39m
[38;5;12m_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mNick[39m[38;5;12m [39m[38;5;12mBostrom[39m

[38;2;255;187;0m[4mQuantum with AI[0m

[38;5;12m- #### Quantum Basic[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum Computing Primer[0m[38;5;12m (https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer#h1-0) - D-Wave quantum computing primer[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum computing 101[0m[38;5;12m (https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101) - Quantum computing 101, from University of Waterloo[39m
[38;5;12m  - [39m[38;5;14m[1mpdf[0m[38;5;12m (https://yadi.sk/i/0VCfWmb3HrrPuw) Quantum Computation and Quantum Information - Nielsen[39m
[38;5;12m  - [39m[38;5;14m[1mpdf[0m[38;5;12m (https://yadi.sk/i/mHoyVef8RaG0aA) 量子计算和量子信息（量子计算部分）- Nielsen[39m
[38;5;12m- #### Quantum AI[39m
[38;5;12m  - [39m[38;5;14m[1mQuantum neural networks[0m[38;5;12m (http://axon.cs.byu.edu/papers/ezhov.fdisis00.pdf)[39m
[38;5;12m  - [39m[38;5;14m[1mAn Artificial Neuron Implemented on an Actual Quantum Processor[0m[38;5;12m (https://arxiv.org/pdf/1811.02266.pdf)[39m
[38;5;12m  - [39m[38;5;14m[1mClassification with Quantum Neural Networks on Near Term Processors[0m[38;5;12m (https://arxiv.org/pdf/1802.06002.pdf)[39m
[38;5;12m  - [39m[38;5;14m[1mBlack Holes as Brains: Neural Networks with Area Law Entropy[0m[38;5;12m (https://arxiv.org/pdf/1801.03918.pdf)[39m
[38;5;12m- #### Quantum Related Framework[39m
[38;5;12m  - [39m[38;5;14m[1mProjectQ[0m[38;5;12m (https://github.com/ProjectQ-Framework/ProjectQ) - ProjectQ is an open source effort for quantum computing.[39m

[38;2;255;187;0m[4mLibs With Online Books[0m
[38;5;12m- #### GC (Generative Content)[39m
[38;5;12m  - [39m[38;5;14m[1mStable Diffusion[0m[38;5;12m (https://github.com/CompVis/stable-diffusion) - [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/2112.10752)[0m[38;5;12m  A latent text-to-image diffusion model[39m
[38;5;12m  - [39m[38;5;14m[1mStable Diffusion V2[0m[38;5;12m (https://github.com/Stability-AI/stablediffusion) - High-Resolution Image Synthesis with Latent Diffusion Models[39m
[38;5;12m  - [39m[38;5;14m[1mGFPGAN[0m[38;5;12m (https://github.com/TencentARC/GFPGAN) - [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/2101.04061)[0m[38;5;12m  GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.[39m
[38;5;12m  - [39m[38;5;14m[1mESRGAN[0m[38;5;12m (https://github.com/xinntao/ESRGAN) - [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/2107.10833)[0m[38;5;12m  ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.[39m
[38;5;12m  - [39m[38;5;14m[1mCodeFormer[0m[38;5;12m (https://github.com/sczhou/CodeFormer) - [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/2206.11253)[0m[38;5;12m  - [39m[38;5;14m[1mNeurIPS 2022[0m[38;5;12m  Towards Robust Blind Face Restoration with Codebook Lookup Transformer[39m
[38;5;12m  - [39m[38;5;14m[1mUniPC[0m[38;5;12m (https://github.com/wl-zhao/UniPC) - [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/2302.04867)[0m[38;5;12m  UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models[39m
[38;5;12m- #### Reinforcement Learning[39m
[38;5;12m  - [39m[38;5;14m[1mA3C[0m[38;5;12m (https://arxiv.org/pdf/1602.01783.pdf) - Google DeepMind Asynchronous Advantage Actor-Critic algorithm[39m
[38;5;12m  [39m[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mQ-Learning[0m[38;5;12m [39m[38;5;12m(http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf)[39m[38;5;12m [39m[38;5;12mSARSA[39m[38;5;12m [39m[38;5;14m[1mDQN[0m[38;5;12m [39m[38;5;12m(https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)[39m[38;5;12m [39m[38;5;14m[1mDDQN[0m[38;5;12m [39m[38;5;12m(https://arxiv.org/pdf/1509.06461.pdf)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mQ-Learning[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mvalue-based[39m[38;5;12m [39m[38;5;12mReinforcement[39m
[38;5;12mLearning[39m[38;5;12m [39m[38;5;12malgorithm[39m
[38;5;12m  - [39m[38;5;14m[1mDDPG[0m[38;5;12m (https://arxiv.org/pdf/1509.02971.pdf) - Deep Deterministic Policy Gradient,[39m
[38;5;12m  - [39m[38;5;14m[1mLarge-Scale Curiosity[0m[38;5;12m (https://arxiv.org/pdf/1808.04355.pdf) - Large-Scale Study of Curiosity-Driven Learning[39m
[38;5;12m  - [39m[38;5;14m[1mPPO[0m[38;5;12m (https://arxiv.org/pdf/1707.06347.pdf) - OpenAI Proximal Policy Optimization Algorithms[39m
[38;5;12m  - [39m[38;5;14m[1mRND[0m[38;5;12m (https://arxiv.org/pdf/1810.12894.pdf) - OpenAI Random Network Distillation, an exploration bonus for deep reinforcement learning method.[39m
[38;5;12m  - [39m[38;5;14m[1mVIME[0m[38;5;12m (https://arxiv.org/pdf/1605.09674.pdf) - OpenAI Variational Information Maximizing Exploration[39m
[38;5;12m  - [39m[38;5;14m[1mDQV[0m[38;5;12m (https://arxiv.org/pdf/1810.00368.pdf) - Deep Quality-Value (DQV) Learning[39m
[38;5;12m  - [39m[38;5;14m[1mERL[0m[38;5;12m (https://arxiv.org/pdf/1805.07917.pdf) - Evolution-Guided Policy Gradient in Reinforcement Learning[39m
[38;5;12m  - [39m[38;5;14m[1mMF Multi-Agent RL[0m[38;5;12m (https://arxiv.org/pdf/1802.05438.pdf) - Mean Field Multi-Agent Reinforcement Learning. (this paper include MF-Q and MF-AC)[39m
[38;5;12m  - [39m[38;5;14m[1mMAAC[0m[38;5;12m (https://arxiv.org/pdf/1810.02912.pdf) - Actor-Attention-Critic for Multi-Agent Reinforcement Learning[39m
[38;5;12m- #### Feature Selection[39m
[38;5;12m  - [39m[38;5;14m[1mscikit-feature[0m[38;5;12m (http://featureselection.asu.edu/algorithms.php) - A collection of feature selection algorithms, available on [39m[38;5;14m[1mGithub[0m[38;5;12m (https://github.com/jundongl/scikit-feature)[39m
[38;5;12m- #### Machine Learning[39m
[38;5;12m  - [39m[38;5;14m[1mScikit learn[0m[38;5;12m (https://scikit-learn.org/stable/) ([39m[38;5;14m[1mPython[0m[38;5;12m) - Machine Learning in Python.[39m
[38;5;12m  - [39m[38;5;14m[1mLinfa[0m[38;5;12m (https://github.com/rust-ml/linfa) ([39m[38;5;14m[1mRust[0m[38;5;12m) - spirit of [39m[48;5;235m[38;5;249mscikit learn[49m[39m[38;5;12m, a rust ML lib.[39m
[38;5;12m  - [39m[38;5;14m[1mXgboost[0m[38;5;12m (https://xgboost.readthedocs.io/en/latest/tutorials/model.html) ([39m[38;5;14m[1mPython, R, JVM, Julia, CLI[0m[38;5;12m) - Xgboost lib's document.[39m
[38;5;12m  - [39m[38;5;14m[1mLightGBM[0m[38;5;12m (https://lightgbm.readthedocs.io/en/latest/Features.html#) ([39m[38;5;14m[1mPython, R, CLI[0m[38;5;12m) - Microsoft lightGBM lib's features document.[39m
[38;5;12m  - [39m[38;5;14m[1mCatBoost[0m[38;5;12m (https://arxiv.org/pdf/1706.09516.pdf) ([39m[38;5;14m[1mPython, R, CLI[0m[38;5;12m) - Yandex Catboost lib's key algorithm pdf papper.[39m
[38;5;12m  - [39m[38;5;14m[1mStackNet[0m[38;5;12m (https://github.com/kaz-Anova/StackNet) ([39m[38;5;14m[1mJava, CLI[0m[38;5;12m) - Some model stacking algorithms implemented in this lib.[39m
[38;5;12m  - [39m[38;5;14m[1mRGF[0m[38;5;12m (https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using [39m[48;5;235m[38;5;249mRegularized Greedy Forest[49m[39m[38;5;12m (multi-core implementation [39m[38;5;14m[1mFastRGF[0m[38;5;12m (https://github.com/RGF-team/rgf/tree/master/FastRGF))[39m
[38;5;12m  [39m[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mFM[0m[38;5;12m [39m[38;5;12m(https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf),[39m[38;5;12m [39m[38;5;14m[1mFastFM[0m[38;5;12m [39m[38;5;12m(https://arxiv.org/pdf/1505.00641.pdf),[39m[38;5;12m [39m[38;5;14m[1mFFM[0m[38;5;12m [39m[38;5;12m(https://arxiv.org/pdf/1701.04099.pdf),[39m[38;5;12m [39m[38;5;14m[1mXDeepFM[0m[38;5;12m [39m[38;5;12m(https://arxiv.org/pdf/1803.05170.pdf)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mFactorization[39m[38;5;12m [39m[38;5;12mMachines[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m
[38;5;12msome[39m[38;5;12m [39m[38;5;12mextended[39m[38;5;12m [39m[38;5;12mAlgorithms[39m
[38;5;12m- #### Deep Learning[39m
[38;5;12m  - [39m[38;5;14m[1mGNN Papers[0m[38;5;12m (https://github.com/thunlp/GNNPapers) - Must-read papers on graph neural networks (GNN)[39m
[38;5;12m  - [39m[38;5;14m[1mEfficientNet[0m[38;5;12m (https://arxiv.org/pdf/1905.11946.pdf) - Rethinking Model Scaling for Convolutional Neural Networks[39m
[38;5;12m  - [39m[38;5;14m[1mDenseNet[0m[38;5;12m (https://arxiv.org/pdf/1608.06993.pdf) - Densely Connected Convolutional Networks[39m
[38;5;12m- #### NLP[39m
[38;5;12m  - [39m[38;5;14m[1mXLNet[0m[38;5;12m (https://arxiv.org/pdf/1906.08237.pdf) - [39m[38;5;14m[1mrepo[0m[38;5;12m (https://github.com/zihangdai/xlnet) XLNet: Generalized Autoregressive Pretraining for Language Understanding[39m
[38;5;12m  - [39m[38;5;14m[1mBERT[0m[38;5;12m (https://arxiv.org/pdf/1810.04805.pdf) - Pre-training of Deep Bidirectional Transformers for Language Understanding[39m
[38;5;12m  - [39m[38;5;14m[1mGPT-3[0m[38;5;12m (https://arxiv.org/pdf/2005.14165.pdf) - Language Models are Few-Shot Learners[39m
[38;5;12m- #### CV[39m
[38;5;12m  - [39m[38;5;14m[1mFast R-CNN[0m[38;5;12m (https://arxiv.org/pdf/1504.08083.pdf) - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection[39m
[38;5;12m  - [39m[38;5;14m[1mMask R-CNN[0m[38;5;12m (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.[39m
[38;5;12m  - [39m[38;5;14m[1mGQN[0m[38;5;12m (http://science.sciencemag.org/content/360/6394/1204/tab-pdf) - DeepMind Generative Query Network, Neural scene representation and rendering[39m
[38;5;12m- #### Meta Learning[39m
[38;5;12m  - [39m[38;5;14m[1mMAML[0m[38;5;12m (https://arxiv.org/pdf/1703.03400.pdf) - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks[39m
[38;5;12m- #### Transfer Learning[39m
[38;5;12m  - [39m[38;5;14m[1mGCN[0m[38;5;12m (https://arxiv.org/pdf/1803.08035.pdf) - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs[39m
[38;5;12m- #### Auto ML[39m
[38;5;12m  - [39m[38;5;14m[1mModel Search[0m[38;5;12m (https://github.com/google/model_search) ([39m[38;5;14m[1mPython[0m[38;5;12m) - Google Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. [39m
[38;5;12m  - [39m[38;5;14m[1mTPOT[0m[38;5;12m (https://github.com/EpistasisLab/tpot) ([39m[38;5;14m[1mPython[0m[38;5;12m) - TPOT is a lib for AutoML.[39m
[38;5;12m  - [39m[38;5;14m[1mAuto-sklearn[0m[38;5;12m (https://automl.github.io/auto-sklearn/master/) ([39m[38;5;14m[1mPython[0m[38;5;12m) - auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator[39m
[38;5;12m  - [39m[38;5;14m[1mAuto-Keras[0m[38;5;12m (https://autokeras.com/) ([39m[38;5;14m[1mPython[0m[38;5;12m) - Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab[39m
[38;5;12m  - [39m[38;5;14m[1mTransmogrifAI[0m[38;5;12m (https://docs.transmogrif.ai/en/stable/index.html) ([39m[38;5;14m[1mJVM[0m[38;5;12m) - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark[39m
[38;5;12m  - [39m[38;5;14m[1mAuto-WEKAA[0m[38;5;12m (http://www.cs.ubc.ca/labs/beta/Projects/autoweka/) - Provides automatic selection of models and hyperparameters for [39m[38;5;14m[1mWEKA[0m[38;5;12m (https://www.cs.waikato.ac.nz/ml/weka/).[39m
[38;5;12m  - [39m[38;5;14m[1mMLBox[0m[38;5;12m (https://github.com/AxeldeRomblay/MLBox) ([39m[38;5;14m[1mPython[0m[38;5;12m) - MLBox is a powerful Automated Machine Learning python library[39m
[38;5;12m- #### Pipeline Training[39m
[38;5;12m  - [39m[38;5;14m[1mZenML[0m[38;5;12m (https://github.com/maiot-io/zenml) ([39m[38;5;14m[1mPython[0m[38;5;12m) - ZenML is built for ML practitioners who are ramping up their ML workflows towards production[39m
[38;5;12m- #### Dimensionality Reduction[39m
[38;5;12m  - [39m[38;5;14m[1mt-SNE[0m[38;5;12m (http://www.cs.toronto.edu/~hinton/absps/tsne.pdf) ([39m[38;5;14m[1mNon-linear/Non-params[0m[38;5;12m) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization[39m
[38;5;12m  - [39m[38;5;14m[1mPCA[0m[38;5;12m (https://www.cs.cmu.edu/~elaw/papers/pca.pdf) ([39m[38;5;14m[1mLinear[0m[38;5;12m) - Principal component analysis[39m
[38;5;12m  - [39m[38;5;14m[1mLDA[0m[38;5;12m (https://www.isip.piconepress.com/publications/reports/1998/isip/lda/lda_theory.pdf) ([39m[38;5;14m[1mLinear[0m[38;5;12m) - Linear Discriminant Analysis[39m
[38;5;12m  - [39m[38;5;14m[1mLLE[0m[38;5;12m (https://cs.nyu.edu/~roweis/lle/papers/lleintro.pdf) ([39m[38;5;14m[1mNon-linear[0m[38;5;12m) - Locally linear embedding[39m
[38;5;12m  - [39m[38;5;14m[1mLaplacian Eigenmaps[0m[38;5;12m (http://web.cse.ohio-state.edu/~belkin.8/papers/LEM_NC_03.pdf) - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[39m
[38;5;12m  - [39m[38;5;14m[1mSammon Mapping[0m[38;5;12m (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) ([39m[38;5;14m[1mNon-linear[0m[38;5;12m) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the[39m
[38;5;12mtwo spaces[39m
[38;5;12m- #### Data Processing[39m
[38;5;12m  - [39m[38;5;14m[1mPandas[0m[38;5;12m (https://github.com/pandas-dev/pandas) ([39m[38;5;14m[1mPython[0m[38;5;12m) - Flexible and powerful data analysis / manipulation library for Python.[39m
[38;5;12m  - [39m[38;5;14m[1mPolars[0m[38;5;12m (https://github.com/pola-rs/polars) ([39m[38;5;14m[1mRust, Python[0m[38;5;12m) - Lightning-fast DataFrame library for Rust and Python.[39m

[38;2;255;187;0m[4mDistributed training[0m
[38;5;12m- [39m[38;5;14m[1mHorovod[0m[38;5;12m (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.[39m
[38;5;12m- [39m[38;5;14m[1mAcme[0m[38;5;12m (https://github.com/deepmind/acme) - A Research Framework for (Distributed) Reinforcement Learning. [39m
[38;5;12m- [39m[38;5;14m[1mbagua[0m[38;5;12m (https://github.com/BaguaSys/bagua) - Bagua is a flexible and performant distributed training algorithm development framework.[39m

[38;2;255;187;0m[4mSupport this project[0m
[38;5;12m![39m[38;5;14m[1mbtc-clean-qrcode[0m[38;5;12m (https://user-images.githubusercontent.com/15725589/152709449-f6b7174b-2990-43f6-ac69-c8549fe7310c.png)[39m
[38;5;12m![39m[38;5;14m[1meth-clean-qrcode[0m[38;5;12m (https://user-images.githubusercontent.com/15725589/152709451-6c2691f9-dec7-4b60-9d20-9fdded828c8c.png)[39m


[38;2;255;187;0m[4mContributors[0m

[38;2;255;187;0m[4mCode Contributors[0m

[38;5;12mThis project exists thanks to all the people who contribute. [39m[38;5;12mContribute[39m[38;5;14m[1m (CONTRIBUTING.md)[0m[38;5;12m .[39m


[38;2;255;187;0m[4mFinancial Contributors[0m

[38;5;12mBecome a financial contributor and help us sustain our community. [39m[38;5;12mContribute[39m[38;5;14m[1m (https://opencollective.com/awesome-AI-books/contribute)[0m[38;5;12m [39m

[38;2;255;187;0m[4mIndividuals[0m



[38;2;255;187;0m[4mOrganizations[0m

[38;5;12mSupport this project with your organization. Your logo will show up here with a link to your website. [39m[38;5;12mContribute[39m[38;5;14m[1m (https://opencollective.com/awesome-AI-books/contribute)[0m[38;5;12m [39m











