/awesome-AI-books

Some awesome AI related books and pdfs for learning

Primary LanguageJupyter Notebook

Awesome AI books

Some awesome AI related books and pdfs for downloading and learning.

Preface

This repo only used for learning, do not use in business, and welcome for providing great books in this repo, if you have great books for sharing, please create PR for me, if any books have new edition, also welcome to update it.

Due to github Large file storage limition issue, all books pdf stored in gitlab repo, please also create PR using git-lfs for gitlab repo

Some often used Mathematic Symbols can refer this page

Content

Organization with papers/researchs

Training ground

  • OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari, Box2d, MuJoCo etc...)
  • DeepMind Pysc2: StarCraft II Learning Environment.
  • TorchCraftAI: A bot platform for machine learning research on StarCraft®: Brood War®
  • Valve Dota2: Dota2 game acessing api. (CN doc)
  • Google Dopamine: Dopamine is a research framework for fast prototyping of reinforcement learning algorithms
  • TextWorld: Microsoft - A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.
  • Mini Grid: Minimalistic gridworld environment for OpenAI Gym

Books

Introductory theory

Mathematics

Data mining

Machine Learning

Deep Learning

Libs With Online Books

  • Reinforcement Learning

    • A3C - Google DeepMind Asynchronous Advantage Actor-Critic algorithm
    • Q-Learning SARSA DQN DDQN - Q-Learning is a value-based Reinforcement Learning algorithm
    • DDPG - Deep Deterministic Policy Gradient,
    • Large-Scale Curiosity - Large-Scale Study of Curiosity-Driven Learning
    • PPO - OpenAI Proximal Policy Optimization Algorithms
    • RND - OpenAI Random Network Distillation, an exploration bonus for deep reinforcement learning method.
    • VIME - OpenAI Variational Information Maximizing Exploration
    • DQV - Deep Quality-Value (DQV) Learning
    • ERL - Evolution-Guided Policy Gradient in Reinforcement Learning
    • MF Multi-Agent RL - Mean Field Multi-Agent Reinforcement Learning. (this paper include MF-Q and MF-AC)
    • MAAC - Actor-Attention-Critic for Multi-Agent Reinforcement Learning

  • Feature Selection

  • Machine Learning

    • Xgboost (Python, R, JVM, Julia, CLI) - Xgboost lib's document.
    • LightGBM (Python, R, CLI) - Microsoft lightGBM lib's features document.
    • CatBoost (Python, R, CLI) - Yandex Catboost lib's key algorithm pdf papper.
    • StackNet (Java, CLI) - Some model stacking algorithms implemented in this lib.
    • RGF - Learning Nonlinear Functions Using Regularized Greedy Forest (multi-core implementation FastRGF)
    • FM, FastFM, FFM, XDeepFM - Factorization Machines and some extended algorithms

  • Deep Learning

    • GNN Papers - Must-read papers on graph neural networks (GNN)
    • DenseNet - Densely Connected Convolutional Networks

  • NLP

    • BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding

  • CV

    • Fast R-CNN - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection
    • Mask R-CNN - Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
    • GQN - DeepMind Generative Query Network, Neural scene representation and rendering

  • Meta Learning

    • MAML - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

  • Transfer Learning

    • GCN - Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

  • Auto ML

    • TPOT (Python) - TPOT is a lib for AutoML.
    • TransmogrifAI (JVM) - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark

  • Dimensionality Reduction

    • t-SNE (Non-linear/Non-params) - T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization
    • PCA (Linear) - Principal component analysis
    • LDA (Linear) - Linear Discriminant Analysis
    • LLE (Non-linear) - Locally linear embedding
    • Laplacian Eigenmaps - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
    • Sammon Mapping (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances in the two spaces