Math for Deep Learning : What You Need to Know to Understand Neural Networks

Kneusel, Ron

No Starch Press,US（2021/12発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 344 p.
• 言語 ENG
• 商品コード 9781718501904
• DDC分類 006.310151

Full Description

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.

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