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

Kneusel, Ronald T.

 
9781718501904: Math for Deep Learning: What You Need to Know to Understand Neural Networks
Tapa blanda
ISBN 10: 1718501900 ISBN 13: 9781718501904
Editorial: No Starch Press, 2021

Sinopsis

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.

 

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Acerca del autor

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).

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
No Starch Press
Año de publicación
2021
Idioma
Inglés
ISBN 10 
1718501900
ISBN 13 
9781718501904
Encuadernación
Tapa blanda
Número de páginas
344
Contacto del fabricante
No Starch Press
https://nostarch.com/python-crash-course-3rd-edition

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