Math for Deep Learning (Paperback)
Language: English
Published by No Starch Press,US, San Francisco, 2021
- Softcover
- New

Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
AbeBooks seller since June 22, 2007
Condition: New
US$ 68.43
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Paperback. 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.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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
Seller Inventory # 9781718501904
- Title
- Math for Deep Learning (Paperback)
- Author
- Ron Kneusel
- Publisher
- No Starch Press,US, San Francisco
- Publication year
- 2021
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1718501900
- ISBN 13
- 9781718501904
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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AussieBookSeller
Truganina, VIC, Australia
AbeBooks seller since June 22, 2007
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The Nile Group Pty Ltd
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Truganina, VIC Australia 3029
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