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awesome-AI-books

https://github.com/dinhtuyen/awesome-AI-books

Last synced: 3 days ago
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Preface
- repo
- page
Books
- Introductory theory
  - 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
  - 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) - %20Sheldon%20M%20Ross.pdf) - Sheldon M Ross
  - 线性代数及其应用 (原书Linear Algebra and Its Applications第3版) - %20David%20C%20Lay.pdf) - David C Lay
  - 统计推断 (原书Statistical Inference第二版) - %20Roger%20Casella.pdf) - Roger Casella
  - 离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版) - %20Kenneth%20H.Rosen.pdf) - 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
- 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
- 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
- Deep Learning
  - 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
  - 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
  - 深度学习 (原书Deep Learning) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Organization with papers/researchs
Training ground
- OpenAI Gym
- Valve Dota2 - cn))
- Mini Grid
- XWorld
- Neural MMO
- TorchCraftAI
- Google Dopamine
- TextWorld - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.
- MinAtar - MinAtar is a testbed for AI agents which implements miniaturized version of several Atari 2600 games.
- craft-env - CraftEnv is a 2D crafting environment
- DeepMind Pysc2
Quantum with AI
- Deep Learning
  - pdf - Nielsen
  - pdf - Nielsen
  - 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
  - Quantum computing 101 - Quantum computing 101, from University of Waterloo
Libs With Online Books
- Deep Learning
  - 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)
  - 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
  - DenseNet - Densely Connected Convolutional Networks
  - 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
  - t-SNE - linear/Non-params**) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization
  - PCA - Principal component analysis
  - LLE - linear**) - Locally linear embedding
  - TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark
  - GCN - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
  - BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding
  - 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
Content
- Distributed training

Programming Languages

Python 5 Jupyter Notebook 3 C++ 2

Ecosyste.ms: Awesome

awesome-AI-books

Preface

Books

Introductory theory

Mathematics

Data mining

Machine Learning

Deep Learning

Organization with papers/researchs

Training ground

Quantum with AI

Deep Learning

Libs With Online Books

Deep Learning

Content