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awesome-AI-books

Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning
https://github.com/zslucky/awesome-AI-books

Last synced: 5 days ago
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Organization with papers/researchs
Training ground
- OpenAI Gym
- DeepMind Pysc2
- Valve Dota2 - cn))
- Mini Grid
- gym-miniworld
- malmo
- Procgen - Generated Game-Like Gym-Environments.
- TorchCraftAI
- Mario AI Framework
- Google Dopamine
- TextWorld - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.
- MAgent - agent Reinforcement Learning
- XWorld
- Neural MMO
- MinAtar
- craft-env
- gym-sokoban
- Pommerman
- gym-miniworld
- vizdoomgym - based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments.
- ddz-ai
- DeepMind Pysc2
- gym-miniworld
Books
- Introductory theory and get start
  - Artificial Intelligence-A Modern Approach (3rd Edition) - Stuart Russell & peter Norvig
  - Grokking Artificial Intelligence Algorithms - Rishal Hurbans
  - Artificial Intelligence-A Modern Approach (3rd Edition) - Stuart Russell & peter Norvig
- Mathematics
  - 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
  - 数理统计学教程 - 陈希儒
  - 数学之美 2th - 吴军
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Linear Algebra and Its Applications (5th) - David C Lay
  - 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
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Convex Optimization - Stephen Boyd
  - A First Course in ProbabilityA First Course in Probability (8th) - Sheldon M Ross
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Linear Algebra and Its Applications (5th) - David C Lay
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - Probability Theory The Logic of Science - Edwin Thompson Jaynes
  - Probability and Statistics 4th - Morris H. DeGroot
  - Convex Optimization - Stephen Boyd
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - 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
  - 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
  - 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
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - 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
  - A First Course in ProbabilityA First Course in Probability (8th) - Sheldon M Ross
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - 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
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - Linear Algebra and Its Applications (5th) - David C Lay
  - 数学之美 2th - 吴军
  - 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
  - Probability Theory The Logic of Science - Edwin Thompson Jaynes
  - 概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版) - Sheldon M Ross
  - 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
  - Probability and Statistics 4th - Morris H. DeGroot
  - Statistical Inference (2nd) - Roger Casella
  - 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
  - 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
  - 凸优化 (原书Convex Optimization) - Stephen Boyd
  - 数理统计学教程 - 陈希儒
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - Linear Algebra and Its Applications (5th) - David C Lay
  - Probability Theory The Logic of Science - Edwin Thompson Jaynes
  - 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
  - A First Course in ProbabilityA First Course in Probability (8th) - Sheldon M Ross
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Linear Algebra and Its Applications (5th) - David C Lay
  - Probability Theory The Logic of Science - Edwin Thompson Jaynes
  - 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
  - 数学之美 2th - 吴军
  - 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
  - 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
  - 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
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - 统计推断 (原书Statistical Inference第二版) - Roger Casella
  - 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
  - 离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版) - Kenneth H.Rosen
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 数理统计学教程 - 陈希儒
  - 凸优化 (原书Convex Optimization) - Stephen Boyd
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - 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
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - Linear Algebra and Its Applications (5th) - David C Lay
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - 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
  - 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
  - Probability Theory The Logic of Science - Edwin Thompson Jaynes
  - Probability and Statistics 4th - Morris H. DeGroot
  - 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
  - Convex Optimization - Stephen Boyd
  - Linear Algebra and Its Applications (5th) - David C Lay
  - Probability and Statistics 4th - Morris H. DeGroot
  - 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
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - 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
  - Linear Algebra and Its Applications (5th) - David C Lay
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - Probability and Statistics 4th - Morris H. DeGroot
  - 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
  - 离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版) - Kenneth H.Rosen
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - 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
  - 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
  - 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
  - 线性代数及其应用 (原书Linear Algebra and Its Applications第3版) - David C Lay
  - Convex Optimization - Stephen Boyd
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - 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
  - 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
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
  - A First Course in ProbabilityA First Course in Probability (8th) - Sheldon M Ross
  - Elements of Information Theory Elements - Thomas Cover & Jay A Thomas
  - Discrete Mathematics and Its Applications 7th - Kenneth H. Rosen
  - 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
- Data mining
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - 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
  - 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
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 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
  - Introduction to Data Mining - Pang-Ning Tan
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Programming Collective Intelligence - Toby Segaran
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - 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
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - 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
  - 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
  - Programming Collective Intelligence - Toby Segaran
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 集体智慧编程 - Toby Segaran
  - Introduction to Data Mining - Pang-Ning Tan
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 集体智慧编程 - Toby Segaran
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - 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
  - 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
  - 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
  - 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
  - 集体智慧编程 - Toby Segaran
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 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
  - 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
  - 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
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - Introduction to Data Mining - Pang-Ning Tan
  - Programming Collective Intelligence - Toby Segaran
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - Introduction to Data Mining - Pang-Ning Tan
  - Feature Engineering for Machine Learning - Amanda Casari, Alice Zheng
  - 集体智慧编程 - Toby Segaran
  - 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
  - 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
  - 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
  - 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
- Machine Learning
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 机器学习 (原书Machine Learning) - Tom M. Mitchell
  - 统计学习方法 - 李航
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 机器学习 - 周志华
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - 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
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 机器学习 (原书Machine Learning) - Tom M. Mitchell
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Information Theory, Inference and Learning Algorithms - David J C MacKay
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - 统计学习方法 - 李航
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 机器学习 - 周志华
  - 统计学习方法 - 李航
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 机器学习 - 周志华
  - 机器学习 (原书Machine Learning) - Tom M. Mitchell
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - 统计学习方法 - 李航
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning - Tom M. Mitchell
  - Pattern Recognition and Machine Learning - Christopher Bishop
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 统计学习方法 - 李航
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - The Elements of Statistical Learning - Trevor Hastie
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Information Theory, Inference and Learning Algorithms - David J C MacKay
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - 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
  - Machine Learning for OpenCV - Michael Beyeler ([Source code here](https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
  - 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
  - Dive into Deep Learning - (Using MXNet)An interactive deep learning book with code, math, and discussions.
  - 动手学深度学习 - (Dive into Deep Learning) for chinese.
  - 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
  - Interpretable AI - Ajay Thampi
  - Conversational AI - Andrew R. Freed
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - d2l-pytorch - (Dive into Deep Learning) pytorch version.
  - 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
  - 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
  - Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
  - 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
  - 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
  - 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
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - 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
  - 神经网络设计 (原书Neural Network Design) - Martin Hagan
  - Learning Deep Architectures for AI - Yoshua Bengio
  - Machine Learning An Algorithmic Perspective (2nd) - Stephen Marsland
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Neural Networks and Learning Machines (3rd) - Simon Haykin
  - 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
  - Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - Learning Deep Architectures for AI - Yoshua Bengio
  - Neural Networks for Applied Sciences and Engineering - Sandhya Samarasinghe
  - Machine Learning An Algorithmic Perspective (2nd) - Stephen Marsland
  - Neural Network Design (2nd) - Martin Hagan
  - 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
  - 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
  - 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
  - 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
  - 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
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - Learning Deep Architectures for AI - Yoshua Bengio
  - Neural Networks and Learning Machines (3rd) - Simon Haykin
  - Neural Networks for Applied Sciences and Engineering - Sandhya Samarasinghe
  - 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
  - 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
  - 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
  - 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
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - Learning Deep Architectures for AI - Yoshua Bengio
  - Machine Learning An Algorithmic Perspective (2nd) - Stephen Marsland
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 神经网络设计 (原书Neural Network Design) - Martin Hagan
  - 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
  - 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
  - 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
  - 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
  - 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
  - 神经网络设计 (原书Neural Network Design) - Martin Hagan
  - 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
  - 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
  - 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
  - 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
  - Neural Network Design (2nd) - Martin Hagan
  - 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 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
  - Deep Learning Methods and Applications - Li Deng & Dong Yu
  - 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
  - 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
  - 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
- Philosophy
  - Human Compatible: Artificial Intelligence and the Problem of Control - Stuart Russell
  - Life 3.0: Being Human in the Age of Artificial Intelligence - Max Tegmark
  - Superintelligence: Paths, Dangers, Strategies - Nick Bostrom
Quantum with AI
- Philosophy
  - Quantum Computing Primer - D-Wave quantum computing primer
  - pdf - Nielsen
  - pdf - Nielsen
  - Quantum neural networks
  - An Artificial Neuron Implemented on an Actual Quantum Processor
  - Classification with Quantum Neural Networks on Near Term Processors
  - Black Holes as Brains: Neural Networks with Area Law Entropy
  - pdf - Nielsen
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  - Quantum Computing Primer - D-Wave quantum computing primer
  - pdf - Nielsen
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Libs With Online Books
- Philosophy
  - A3C - Google DeepMind Asynchronous Advantage Actor-Critic algorithm
  - Q-Learning - media/dqn/DQNNaturePaper.pdf) [DDQN](https://arxiv.org/pdf/1509.06461.pdf) - 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)
  - MAAC - Actor-Attention-Critic for Multi-Agent Reinforcement Learning
  - scikit-feature - A collection of feature selection algorithms, available on [Github](https://github.com/jundongl/scikit-feature)
  - Scikit learn - Machine Learning in Python.
  - Xgboost - Xgboost lib's document.
  - LightGBM - Microsoft lightGBM lib's features document.
  - CatBoost - Yandex Catboost lib's key algorithm pdf papper.
  - RGF - Learning Nonlinear Functions Using `Regularized Greedy Forest` (multi-core implementation [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF))
  - FM - Factorization Machines and some extended Algorithms
  - EfficientNet - Rethinking Model Scaling for Convolutional Neural Networks
  - DenseNet - Densely Connected Convolutional Networks
  - XLNet - [repo](https://github.com/zihangdai/xlnet) XLNet: Generalized Autoregressive Pretraining for Language Understanding
  - GPT-3 - Language Models are Few-Shot Learners
  - 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
  - MAML - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
  - GCN - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
  - Auto-sklearn - auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator
  - Auto-Keras - Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab
  - TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark
  - Auto-WEKAA - Provides automatic selection of models and hyperparameters for [WEKA](https://www.cs.waikato.ac.nz/ml/weka/).
  - ZenML - ZenML is built for ML practitioners who are ramping up their ML workflows towards production
  - t-SNE - linear/Non-params**) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization
  - PCA - Principal component analysis
  - LDA - Linear Discriminant Analysis
  - LLE - linear**) - Locally linear embedding
  - Sammon Mapping - linear**) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the
  - Fast R-CNN - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection
  - MAML - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
  - GCN - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
Distributed training
- Philosophy
  - Acme - A Research Framework for (Distributed) Reinforcement Learning.
Support this project
- Philosophy
  - btc-clean-qrcode
  - eth-clean-qrcode

Programming Languages

Python 11 Jupyter Notebook 3 C++ 3 Java 2

Categories

Books 2,056 Quantum with AI 121 Libs With Online Books 39 Training ground 23 Organization with papers/researchs 5 Support this project 2 Distributed training 1

Sub Categories

Mathematics 783 Deep Learning 594 Machine Learning 448 Data mining 225 Philosophy 166 Introductory theory and get start 3

Keywords

reinforcement-learning 6 deep-learning 2 sokoban 1 python 1 openai 1 gym 1 environment 1 paper 1 simulator 1 multi-agent 1 text-based-game 1 text-based-adventure 1 tensorflow 1 rl 1 ml 1 google 1 ai 1 starcraft-ii-replays 1 starcraft-ii 1 machine-learning 1 deepmind 1 blizzard-api 1 pytorch-implmention 1 pytorch 1 nlp 1 mxnet 1 dive-into-deep-learning 1 data-science 1 d2l 1 computer-vision 1 book 1 vizdoom 1 openai-gym-environments 1 openai-gym 1