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Hands-On Java Deep Learning for Computer Vision
book

Hands-On Java Deep Learning for Computer Vision

by Klevis Ramo
February 2019
Intermediate to advanced content levelIntermediate to advanced
260 pages
6h 3m
English
Packt Publishing
Content preview from Hands-On Java Deep Learning for Computer Vision

Optimizing algorithms

In the previous section, we learned how to normalize the data and initialize the weights, along with choosing a good activation function that can dramatically speed up neural network learning time.

In this section, we'll take a step further by optimizing our algorithm and the way we update the weights in a backward pass.

To do this, we need to revisit how a neural network learns. We begin with training data of size m, and each of the examples depicted in this section has n features, and for each of the examples, we also have its corresponding prediction value:

What we want is a neural network to learn from these examples ...

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Publisher Resources

ISBN: 9781789613964Supplemental Content