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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

Putting it all together with an example

As we already mentioned, multi-layer neural networks can classify linearly separable classes. In fact, the Universal Approximation Theorem states that any continuous functions on compact subsets of Rn can be approximated by a neural network with at least one hidden layer. The formal proof of such a theorem is too complex to be explained here, but we'll attempt to give an intuitive explanation using some basic mathematics. We'll implement a neural network that approximates the boxcar function, in the following diagram on the right, which is a simple type of step function. Since a series of step functions can approximate any continuous function on a compact subset of R, this will give us an idea of why ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content