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Python Deep Learning - Second Edition
book

Python Deep Learning - Second Edition

by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
January 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
11h 13m
English
Packt Publishing
Content preview from Python Deep Learning - Second Edition

Residual networks

Residual networks (ResNets, https://arxiv.org/abs/1512.03385) were released in 2015, when they won all five categories of the ImageNet challenge that year. In Chapter 2, Neural Networks, we mentioned that the layers of a neural network are not restricted to sequential order, but form a graph instead. This is the first architecture we'll learn, which takes advantage of this flexibility. This is also the first network architecture that has successfully trained a network with the depth of more than 100 layers.

Thanks to better weight initializations, new activation functions, as well as normalization layers, it's now possible to train deep networks. But the authors of the paper conducted some experiments and observed that a ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content