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.
