To prepare for your dive into deep learning, you will need a few survival skills: (i) techniques for storing and manipulating data; (ii) libraries for ingesting and preprocessing data from a variety of sources; (iii) knowledge of the basic linear algebraic operations that we apply to high-dimensional data elements; (iv) just enough calculus to determine which direction to adjust each parameter in order to decrease the loss function; (v) the ability to automatically compute derivatives so that you can forget much of the calculus you just learned; (vi) some basic fluency in probability, our primary language for reasoning under uncertainty; and (vii) some aptitude for finding answers in the official documentation when you get stuck.
In short, this chapter provides a rapid introduction to the basics that you will need to follow most of the technical content in this book.
- 2.1. Data Manipulation
- 2.2. Data Preprocessing
- 2.3. Linear Algebra
- 2.4. Calculus
- 2.5. Automatic Differentiation
- 2.6. Probability and Statistics
- 2.7. Documentation