Math for Deep Learning (Paperback)

Ron Kneusel

ISBN 10: 1718501900 / ISBN 13: 9781718501904

Published by No Starch Press,US, San Francisco, 2021

About this Item

Paperback. 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.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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781718501904

Bibliographic Details

• Title: Math for Deep Learning (Paperback)
• Publisher: No Starch Press,US, San Francisco
• Publication Date: 2021
• Language: English
• ISBN 10: 1718501900
• ISBN 13: 9781718501904
• Binding: Paperback
• Condition: new

About this title

Synopsis

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 earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021).