Math for Deep Learning

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.

With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.

You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.

In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta

About the Author

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021).

Table of contents

Introduction

Chapter 1: Setting the Stage

Chapter 2: Probability

Chapter 3: More Probability

Chapter 4: Statistics

Chapter 5: Linear Algebra

Chapter 6: More Linear Algebra

Chapter 7: Differential Calculus

Chapter 8: Matrix Calculus

Chapter 9: Data Flow in Neural Networks

Chapter 10: Backpropagation

Chapter 11: Gradient Descent

Appendix: Going Further