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Deep Learning with Keras

You're reading from Deep Learning with Keras

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787128422

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Antonio Gulli

Sujit Pal

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Table of Contents (16) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Neural Networks Foundations

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

Keras 2.0 — what is new

Summary

In this chapter, we discussed how to install Theano, TensorFlow, and Keras on the following:

Your local machine
A dockerized infrastructure based on containers
In the cloud with Google GCP, Amazon AWS, and Microsoft Azure

In addition to that, we looked at a few modules defining Keras APIs and some commonly useful operations such as loading and saving neural networks' architectures and weights, early stopping, history saving, checkpointing, interactions with TensorBoard, and interactions with Quiver.

In the next chapter, we will introduce the concept of convolutional networks a fundamental innovation in deep learning which has been used with success in multiple domains from text, to video, to speech going well beyond the initial image processing domain where they were originally conceived.

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Authors (2)

Antonio Gulli

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.

See other products by Antonio Gulli

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

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