Introduction

Modern technology would be impossible without math. In fact, tech fields like data science, artificial intelligence, cryptography, computer graphics, and many more, are rooted in it. Needless to say, the price of admission into any of these exciting fields includes strong math skills. To help you get familiar with the math you need, I’ve put together a sampler containing chapters from three Manning books that feature just a few applications of math in the technology we depend on—and even help develop!— every day.

The first chapter, from Algorithms and Data Structures in Action by research scientist and algorithm expert Marcello La Rocca, dives right into nearest neighbors search, a common machine learning algorithm widely used in a variety of applications including computer vision, statistical classification, DNA sequencing, and many more.

In the next chapter, from Math and Architectures of Deep Learning, deep learning expert Krishnendu Chaudhury explains what vectors, matrices, and tensors are, as well as why they’re important, with the aid of offers an up-close-and-personal examination of Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval, a fundamental and widely used solution for NLP problems.

In the last chapter, from my own book, Math for Programmers, you’re going to work with digital audio data, using mathematical functions in different and really interesting ways, including using the Fourier series to determine what musical notes a sound wave function contains.

When you’re done, I hope you’re inspired to pursue the math knowledge and skills that will give you a competitive edge in the field of technology that interests you the most. If you’d like to explore any or all of these subjects in more depth, the three books featured in this sampler are a great place to start!

Thanks for reading!

—Paul Orland