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

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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.
Publisher ‏ : ‎ No Starch Press
Publication date ‏ : ‎ December 7, 2021
Language ‏ : ‎ English
Print length ‏ : ‎ 344 pages
ISBN-10 ‏ : ‎ 1718501900
ISBN-13 ‏ : ‎ 978-1718501904
Item Weight ‏ : ‎ 1.26 pounds
Dimensions ‏ : ‎ 7.01 x 0.75 x 9.25 inches
Best Sellers Rank: #565,602 in Books (See Top 100 in Books) #180 in Computer Neural Networks #257 in Calculus (Books) #1,737 in Computer Science (Books)
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