Introduction

Deep learning is a subset of machine learning that focuses on using neural networks to solve complex data problems. It is becoming increasingly popular nowadays, thanks to advances in software and hardware that allow us to gather and process large amounts of data (we are talking about millions and billions of entries). This is important considering that deep neural networks require vast amounts of data to perform well.

Some of the most well-known applications of deep learning are self-driving vehicles, popular chatbots, and a wide variety of voice-activated assistants, which will be further explained in this chapter.

PyTorch was launched back in 2017, and its main characteristic is that it uses graphics processing units (GPUs) to process data using "tensors". This allows algorithms to run at high speeds and, at the same time, it provides its users with flexibility and a standard syntax to obtain the best results for many data problems. Furthermore, PyTorch uses dynamic computational graphs that allow you to make changes to the network on the go. This book demystifies neural networks using PyTorch and helps you understand the complexity of neural network architectures.