deepLearningBook

deep learning 学习笔记

• 学习资料来自： - 英文：http://www.deeplearningbook.org/ - 完整版pdf下载 - 中文： - https://hit-scir.gitbooks.io/neural-networks-and-deep-learning-zh_cn/content/index.html - 深度 | 深度学习与神经网络全局概览：核心技术的发展历程 - 同时也顺便学习用LaTex写公式：

目录：

• Table of Contents:更详细的目录列表
• Acknowledgements
• Notation: 使用到的符号说明
• 1 Introduction：
• Part I: Applied Math and Machine Learning Basics - 2 Linear Algebra - 3 Probability and Information Theory - 4 Numerical Computation - 5 Machine Learning Basics
• Part II: Modern Practical Deep Networks - 6 Deep Feedforward Networks - 7 Regularization for Deep Learning - 8 Optimization for Training Deep Models - 9 Convolutional Networks - 10 Sequence Modeling: Recurrent and Recursive Nets - 11 Practical Methodology - 12 Applications
• Part III: Deep Learning Research - 13 Linear Factor Models - 14 Autoencoders - 15 Representation Learning - 16 Structured Probabilistic Models for Deep Learning - 17 Monte Carlo Methods - 18 Confronting the Partition Function - 19 Approximate Inference - 20 Deep Generative Models