To get started with deep learning, we will need to develop a few basic skills. All machine learning is concerned with extracting information from data. So we will begin by learning the practical skills for storing, manipulating, and preprocessing data.
Moreover, machine learning typically requires working with large datasets, which we can think of as tables, where the rows correspond to examples and the columns correspond to attributes. Linear algebra gives us a powerful set of techniques for working with tabular data. We will not go too far into the weeds but rather focus on the basic of matrix operations and their implementation.
Additionally, deep learning is all about optimization. We have a model
with some parameters and we want to find those that fit our data the
best. Determining which way to move each parameter at each step of an
algorithm requires a little bit of calculus, which will be briefly
introduced. Fortunately, the
autograd package automatically computes
differentiation for us, and we will cover it next.
Next, machine learning is concerned with making predictions: what is the likely value of some unknown attribute, given the information that we observe? To reason rigorously under uncertainty we will need to invoke the language of probability.
In the end, the official documentation provides plenty of descriptions and examples that are beyond this book. To conclude the chapter, we will show you how to look up documentation for the needed information.
This book has kept the mathematical content to the minimum necessary to get a proper understanding of deep learning. However, it does not mean that this book is mathematics free. Thus, this chapter provides a rapid introduction to basic and frequently-used mathematics to allow anyone to understand at least most of the mathematical content of the book. If you wish to understand all of the mathematical content, further reviewing the online appendix on mathematics should be sufficient.