Math for Deep Learning: What You Need to Know to Understand Neural Networks
Author: Ronald T. Kneusel (Author)
Publisher finelybook 出版社: No Starch Press
Publication Date 出版日期: 2021-12-07
Language 语言: English
Print Length 页数: 344 pages
ISBN-10: 1718501900
ISBN-13: 9781718501904
Book 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.
Review
—Daniel Gutierrez, insideBIGDATA
“Ronald T. Kneusel has written a handy and compact guide to the mathematics of deep learning. It will be a well-worn reference for equations and algorithms for the student, scientist, and practitioner of neural networks and machine learning. Complete with equations, figures and even sample code in Python, this book is a wonderful mathematical introduction for the reader.”
“What makes
Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach.”—Ed Scott, Ph.D., Solutions Architect & IT Enthusiast
About the Author
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