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

iebukes No Starch Press 49 次浏览 没有评论
Math for Deep Learning: What You Need to Know to Understand Neural Networks Front Cover

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

by Ronald T. Kneusel
  • Length: 344 pages
  • Edition: 1
  • Publisher: No Starch Press
  • Publication Date: 2021-11-23
  • ISBN-10: 1718501900
  • ISBN-13: 9781718501904
  • Sales Rank: #701741 (See Top 100 Books)
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.

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

 
 扫描二维码,关注微信公众号,发送“FWMM”获取下载访问密码,关注我,永不迷路  
                微信公众号二维码