Download the fantastic book titled Math for Deep Learning written by Ronald T. Kneusel, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Math for Deep Learning", which was released on 07 December 2021. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.
Summary of Math for Deep Learning by Ronald T. Kneusel PDF
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.
Detail About Math for Deep Learning PDF
- Author : Ronald T. Kneusel
- Publisher : No Starch Press
- Genre : Computers
- Total Pages : 346 pages
- ISBN : 1718501900
- Release Date : 07 December 2021
- PDF File Size : 39,6 Mb
- Language : English
- Rating : 4/5 from 21 reviews
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