Math for Deep Learning: What You Need to Know to Understand Neural Networks

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. 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.

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Location: East Hanover, New Jersey

End Time: 2024-11-29T02:59:46.000Z

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EAN: 9781718501904

UPC: 9781718501904

ISBN: 9781718501904

MPN: N/A

Number of Pages: 344 Pages

Publication Name: Math for Deep Learning : What You Need to Know to Understand Neural Networks

Language: English

Publisher: No Starch Press, Incorporated

Publication Year: 2021

Subject: Neural Networks, General, Calculus

Item Height: 0.9 in

Type: Textbook

Item Weight: 23.2 Oz

Item Length: 9.1 in

Subject Area: Mathematics, Computers, Science

Author: Ronald T. Kneusel

Item Width: 7 in

Format: Trade Paperback