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Math For Deep Learning:What You Need To Know To Understand Neural Networks
[Paperback - 2021]
ronald t. kneusel
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List Price: $49.99
Our Price: Rs.11595 Rs.9856
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Category: Computer
Sub-category: Computer Science
Additional Category: Mathematics - Networking
Publisher: No Starch Press | ISBN: 9781718501904 | Pages: 344
Shipping Weight: .649 | Dimensions: 7.06 x .77 x 9.25 inches

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.

 

About the Author

Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.

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