| Math for Deep Learning (No Starch Press) |
| Friday, 17 December 2021 | |||
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This book, subtitled "What You Need to Know to Understand Neural Networks" provides the essential math to follow deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. Ronald T. Kneusel uses Python examples to explain 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. <ASIN: 1718501900> The book also covers gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta. Author: Ronald T. Kneusel
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