Math for Deep Learning : What You Need to Know to Understand Neural Networks
by
Ron Kneusel
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
Publisher Out of Stock
Delivery Location
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Secure
Quality
Fast
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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