AI and ML for Coders in PyTorch

Chapter 1. Introduction to PyTorch

When it comes to creating artificial intelligence (AI), machine learning (ML) and deep learning are great places to begin. When you’re getting started, however, it’s easy to get overwhelmed by the options and all the new terminology. This book aims to demystify things for you as a programmer. It takes you through writing code to implement concepts of ML and deep learning, and it also takes you through building models that behave more as a human does, with scenarios like computer vision, natural language processing (NLP), and more. Thus, these models become a form of synthesized, or artificial, intelligence.

But when we refer to machine learning, what exactly is it? Let’s take a quick look at that and consider it from a programmer’s perspective before we go any further. After that, in the rest of this chapter, we’ll show you how to install the tools of the trade, from PyTorch itself to environments where you can code and debug your PyTorch-based models.

What Is Machine Learning?

Before we get into the ins and outs of ML, let’s consider how it evolved from traditional programming. We’ll start by examining what traditional programming is, and then we’ll consider cases where it’s limited. After that, we’ll see how ML evolved to handle those cases and thus opened up new opportunities to implement new scenarios, thereby unlocking many of the concepts of AI.

Traditional programming involves writing rules that are expressed in a programming language ...