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    Part-I-Applied-Math-and-Machine-Learning-Basics
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    1-Introduction
    
    5-Machine-Learning-Basics
    5-Machine-Learning-Basics
    
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    5.1-Learning-Algorithms
    
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    5.5-Maximum-Likelihood-Estimation
    
    5.5-Maximum-Likelihood-Estimation
    
    Part-II-Deep-Networks-Modern-Practices
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    6-Deep-Feedforward-Networks
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    Universal-approximation-theorem
    
    6.5-Back-Propagation-and-Other-Differentiation-algorithms
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    Back-Propagation
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    9-Convolutional-Networks
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    paper-Convolutional-Sequence-to-Sequence-Learning
    
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    10-Sequence-Modeling-Recurrentand-Recursive-Nets
    
    10.1-Unfolding-Computational-Graphs
    
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    10.10-The-Long-Short-Term-Memory-and-Other-Gated
    
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    12-Applications
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    12.4-Natural-Language-Processing
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    whitepaper2015
    
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    tf.placeholder-VS-tf.Variable
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    Introduction
    
    How-to-understand-tf.Variable
    
    TODO-C++
    TODO-C++
    
    Introduction
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  pytorch
  - paper-Automatic-differentiation-in-PyTorch
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Data-set
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Application
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    Entity-Linking
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    paper-Bidirectional-LSTM-with-attention-mechanism-and-convolutional-layer
    
    Part-of-speech-tagging
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    Introduction
    
    Neural-machine-translation
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    Distant-supervision
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    Distant-supervision
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    Part-of-speech
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    BERT
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    openai-GPT-2
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    BERT
    BERT
    
    BERT
    
    BERT-paper
    
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    GPT
    GPT
    
    openai-GPT-2
  - Book-Natural-Language-Processing-with-Python
    Book-Natural-Language-Processing-with-Python
    
    Introduction
- Computable-knowledge
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- Sequence
  Sequence
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- NRE
  NRE
  - Introduction

前言

本章介绍机器学习的相关的理论知识。

概率论与数理统计：在Probability-theory-and-Statistics章节

深度学习理论：在Deep-learning章节