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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

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787128422

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

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

1. Neural Networks Foundations

2. Keras Installation and API FREE CHAPTER

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

Keras API

Keras has a modular, minimalist, and easy extendable architecture. Francois Chollet, the author of Keras, says:

The library was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Keras defines high-level neural networks running on top of either TensorFlow (for more information, refer to https://github.com/tensorflow/tensorflow) or Theano (for more information, refer to https://github.com/Theano/Theano). In details:

Modularity: A model is either a sequence or a graph of standalone modules that can be combined together like LEGO blocks for building neural networks. Namely, the library predefines a very large number of modules implementing different types of neural layers, cost functions, optimizers, initialization schemes, activation functions, and regularization schemes.
Minimalism: The library is implemented in Python and each module is kept short and self-describing.
Easy extensibility...

You have been reading a chapter from

Deep Learning with Keras

Published in: Apr 2017

Publisher: Packt

ISBN-13: 9781787128422

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Authors (2)

Antonio Gulli

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

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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