Kneusel Ronald T. Math For Deep Learning: What You Need to Know to Understand Neural Networks

No Starch Press, 2022. — 344 p. — ISBN: 978-1-7185-0190-4.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.