Deep Learning Cookbook

Chapter 1 provides in-depth information about how neural networks function, where to get data from, and how to preprocess that data to make it easier to consume. Chapter 2 is about getting stuck and what to do about it. Neural nets are notoriously hard to debug and the tips and tricks in this chapter on how to make them behave will come in handy when going through the more project-oriented recipes in the rest of the book. If you are impatient, you can skip this chapter and go back to it later when you do get stuck. Chapters 3 through 15 are grouped around media, starting with text rocessing, followed by image processing, and finally music processing in Chapter 15. Each chapter describes one project split into various recipes. Typically a chapter will start with a data acquisition recipe, followed by a few recipes that build toward the goal of the chapter and a recipe on data visualization. Chapter 16 is about using models in production. Running experiments in notebooks is great, but ultimately we want to share our results with actual users and get our models run on real servers or mobile devices. This chapter goes through the options.