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

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Marque : GENERIC
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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 ...

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Description produit
Marque
GENERIC
Titre principal
Math for Deep Learning: What You Need to Know to Understand Neural Networks
Editeur
No Starch Press
Type de produit
Paperback
Présentation du livre
Paperback
Release date
12/7/2021 12:00:00 AM
Langue d'origine
English
ISBN
1718501900
Dimensions
7.01 x 0.75 x 9.25 inches
Nombre de pages de livre
344 pages
Langue - Librairie
English
Résumé
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. Read more
Auteur(s)
Ronald T. Kneusel
Date de parution
12/7/2021 12:00:00 AM