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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

Summary

In this chapter, you learned the basics of neural networks, more specifically, what a perceptron is, what a multilayer perceptron is, how to define neural networks in Keras, how to progressively improve metrics once a good baseline is established, and how to fine-tune the hyperparameter's space. In addition to that, you now also have an intuitive idea of what some useful activation functions (sigmoid and ReLU) are, and how to train a network with backpropagation algorithms based on either gradient descent, on stochastic gradient descent, or on more sophisticated approaches, such as Adam and RMSprop.

In the next chapter, we will see how to install Keras on AWS, Microsoft Azure, Google Cloud, and on your own machine. In addition to that, we will provide an overview of Keras APIs.

You have been reading a chapter from
Deep Learning with Keras
Published in: Apr 2017
Publisher: Packt
ISBN-13: 9781787128422
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