Deep Learning with PyTorch
In the previous chapter, you became familiar with open source libraries, which provided you with a collection of reinforcement learning (RL) environments. However, recent developments in RL, and especially its combination with deep learning (DL), now make it possible to solve much more challenging problems than ever before. This is partly due to the development of DL methods and tools. This chapter is dedicated to one such tool, PyTorch, which enables us to implement complex DL models with just a bunch of lines of Python code.
The chapter doesn't pretend to be a complete DL manual, as the field is very wide and dynamic; however, we will cover:
- The PyTorch library specifics and implementation details (assuming that you are already familiar with DL fundamentals)
- Higher-level libraries on top of PyTorch, with the aim of simplifying common DL problems
- The library PyTorch ignite, which will be used in some examples