## 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

## 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 *).