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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

View More author details

Table of Contents (16) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Neural Networks Foundations

Keras Installation and API

Deep Learning with ConvNets

Generative Adversarial Networks and WaveNet

Word Embeddings

Recurrent Neural Network — RNN

Additional Deep Learning Models

AI Game Playing

Conclusion

Keras 2.0 — what is new

Keras adversarial GANs for forging MNIST

Keras adversarial (https://github.com/bstriner/keras-adversarial) is an open source Python package for building GANs developed by Ben Striner (https://github.com/bstriner and https://github.com/bstriner/keras-adversarial/blob/master/LICENSE.txt). Since Keras just recently moved to 2.0, I suggest downloading latest Keras adversarial package:

git clone --depth=50 --branch=master https://github.com/bstriner/keras-adversarial.git

And install setup.py:

python setup.py install

Note that compatibility with Keras 2.0 is tracked in this issue https://github.com/bstriner/keras-adversarial/issues/11.

If the generator G and the discriminator D are based on the same model, M, then they can be combined into an adversarial model; it uses the same input, M, but separates targets and metrics for G and D. The library has the following API call:

adversarial_model = AdversarialModel(base_model=M,
    player_params=[generator.trainable_weights, discriminator.trainable_weights...

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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.

See other products by Sujit Pal

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