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Math for Deep Learning

By Kneusel, Ronald T.

Math for Deep Learning

By Kneusel, Ronald T.

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
344
Release Date:
2021-12-07
Publication Date:
2021-12-07
Publisher:
Random House LLC US
Languages:
Original: English
ISBN10:
1718501900
ISBN13:
9781718501904
GPSR Manufacturer Reference:
[email protected]
Weight:
562 g
Height:
177 cm
Width:
232 cm
Thickness:
24 cm
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