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Deep Learning with Keras

You're reading from   Deep Learning with Keras Implementing deep learning models and neural networks with the power of Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787128422
Length 318 pages
Edition 1st Edition
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Authors (2):
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Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
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Table of Contents (10) Chapters Close

Preface 1. Neural Networks Foundations FREE CHAPTER 2. Keras Installation and API 3. Deep Learning with ConvNets 4. Generative Adversarial Networks and WaveNet 5. Word Embeddings 6. Recurrent Neural Network — RNN 7. Additional Deep Learning Models 8. AI Game Playing 9. Conclusion

Generative models


Generative models are models that learn to create data similar to data it is trained on. We saw one example of a generative model that learns to write prose similar to Alice in Wonderland in Chapter 6, Recurrent Neural Network — RNN. In that example, we trained a model to predict the 11th character of text given the first 10 characters. Yet another type of generative model is generative adversarial models (GAN) that have recently emerged as a very powerful class of models—you saw examples of GANs in Chapter 4, Generative Adversarial Networks and WaveNet. The intuition for generative models is that it learns a good internal representation of its training data, and is therefore able to generate similar data during the prediction phase.

Another perspective on generative models is the probabilistic one. A typical classification or regression network, also called a discriminative model, learns a function that maps the input data X to some label or output y, that is, these models...

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