Deep Learning for Coders with fastai and PyTorch

Chapter 2. From Model to Production

The six lines of code we saw in Chapter 1 are just one small part of the process of using deep learning in practice. In this chapter, we’re going to use a computer vision example to look at the end-to-end process of creating a deep learning application. More specifically, we’re going to build a bear classifier! In the process, we’ll discuss the capabilities and constraints of deep learning, explore how to create datasets, look at possible gotchas when using deep learning in practice, and more. Many of the key points will apply equally well to other deep learning problems, such as those in Chapter 1. If you work through a problem similar in key respects to our example problems, we expect you to get excellent results with little code, quickly.

Let’s start with how you should frame your problem.

The Practice of Deep Learning

We’ve seen that deep learning can solve a lot of challenging problems quickly and with little code. As a beginner, there’s a sweet spot of problems that are similar enough to our example problems that you can very quickly get extremely useful results. However, deep learning isn’t magic! The same six lines of code won’t work for every problem anyone can think of today.

Underestimating the constraints and overestimating the capabilities of deep learning may lead to frustratingly poor results, at least until you gain some experience and can solve the problems that arise. Conversely, overestimating the constraints and underestimating ...