Kneusel, Ronald T
Math for Deep Learning: What You Need to Know to Understand Neural Networks
- ISBN 13:
- 9781718501904
- author:
- Kneusel, Ronald T
- format:
- Paperback / softback
- publisher:
- No Starch Press
- language:
- English
- Publication Year:
- 2021
- Pages:
- 344
- Dimensions:
- 23.1 x 17.8 x 2.3 centimetres (0
- Genre:
- Science, Mathematics, Calculus,
- Condition:
- New
- Availability:
- Item usually sent within 10 working days
Description
Deep learning relies heavily on mathematical concepts. Math for Deep Learning provides the essential math needed to understand and work with deep learning discussions, enabling you to explore more complex implementations and better use deep learning toolkits. You'll learn key deep learning-related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus through Python examples. This includes how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also work with the mathematics underlying these algorithms using Python, building a fully-functional neural network. In addition, you'll find coverage of gradient descent, including variations commonly used by the deep learning community such as SGD, Adam, RMSprop, and Adagrad/Adadelta.
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