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.
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