Math for Deep Learning (ePub)
What You Need to Know to Understand Neural Networks
(Sprache: Englisch)
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...
With Math for Deep Learning, you'll learn the essential mathematics used...
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Produktinformationen zu „Math for Deep Learning (ePub)“
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.
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.
Autoren-Porträt von Ronald T. Kneusel
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).
Bibliographische Angaben
- Autor: Ronald T. Kneusel
- 2021, 344 Seiten, Englisch
- Verlag: No Starch Press
- ISBN-10: 1718501919
- ISBN-13: 9781718501911
- Erscheinungsdatum: 23.11.2021
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 24 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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