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.
