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Hands-On Mathematics for Deep Learning
book

Hands-On Mathematics for Deep Learning

by Jay Dawani
June 2020
Intermediate to advanced
364 pages
13h 56m
English
Packt Publishing
Content preview from Hands-On Mathematics for Deep Learning

Generative Models

So far in this book, we have covered the three main types of neural networks—feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Each of them are discriminative models; that is, they learned to discriminate (differentiate) between the classes we wanted them to be able to predict, such as is this language French or English?, is this song classic rock or 90s pop?, and what are the objects present in this scene?. However, deep neural networks don't just stop there. They can also be used to improve image or video resolution or generate entirely new images and data. These types of models are known as generative models.

In this chapter, we will cover the following topics ...

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Publisher Resources

ISBN: 9781838647292