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

Book Details

Format Paperback / Softback
ISBN-10 1718501900
ISBN-13 9781718501904
Publisher No Starch Press,US
Imprint No Starch Press,US
Country of Manufacture CN
Country of Publication GB
Publication Date Dec 7th, 2021
Print length 344 Pages
Weight 646 grams
Dimensions 18.00 x 23.30 x 2.30 cms
Product Classification: Neural networks & fuzzy systems
Ksh 8,650.00
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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.
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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