Summary

In this chapter, you saw a quick overview of PyTorch's functionality and features. We talked about basic fundamental pieces, such as tensors and gradients, and you saw how an NN can be made from the basic building blocks, before learning how to implement those blocks yourself.

We discussed loss functions and optimizers, as well as the monitoring of training dynamics. Finally, you were introduced to PyTorch Ignite, a library used to provide a higher-level interface for training loops. The goal of the chapter was to give a very quick introduction to PyTorch, which will be used later in the book.

In the next chapter, we are ready to start dealing with the main subject of this book: RL methods.