Authors

• Jingqing Zhang - Imperial College London

• Hang Yuan - Oxford University

• Hao Dong* - Peking University (hao.dong[at]pku.edu.cn)

Abstract

This chapter aims to briefly introduce the fundamentals for deep learning, which is the key component of deep reinforcement learning. We will start with a naive single-layer network and gradually progress to much more complex but powerful architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We will end this chapter with a couple of examples that demonstrate how to implement deep learning models in practice.

Keywords: deep learning, convolutional neural networks, recurrent neural networks

Content

中文版PDF

Citation

To cite this book, please use this bibtex entry:

@incollection{deepRL-chapter1-2020,
 title={Introduction to Deep Learning},
 chapter={1},
 author={Jingqing Zhang, Hang Yuan, Hao Dong},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},
 pages={3-46},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}

If you find any typos or have suggestions for improving the book, do not hesitate to contact with the corresponding author (name with *).