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Math for Deep Learning: A Practitioner's Guide to Mastering Neural Networks

Ronald T. Kneusel

4.47/5 (29 ratings)

Read Now Download Now

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.

Format:: Paperback

Pages:: pages

Publication:: 2021

Publisher:: Penguin Random House Publisher Services

Edition:

Language:: eng

ISBN10:: 1718501900

ISBN13:: 9781718501904

kindle Asin:: B096JXMQLM

Math for Deep Learning: A Practitioner's Guide to Mastering Neural Networks

Ronald T. Kneusel

4.47/5 (29 ratings)

Read Download

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.

Format:: Paperback

Pages:: pages

Publication:: 2021

Publisher:: Penguin Random House Publisher Services

Edition:

Language:: eng

ISBN10:: 1718501900

ISBN13:: 9781718501904

kindle Asin:: B096JXMQLM

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