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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

Chapter 9. Conclusion

Congratulations on making it to the end of the book! Let us take a moment and see how far we have come since we started.

If you are like most readers, you started with some knowledge of Python and some background in machine learning, but you were interested in learning more about deep learning and wanted to be able to apply these deep learning skills using Python.

You learned how to install Keras on your machine and started using it to build simple deep learning models. You then learned about the original deep learning model, the multi-layer perceptron, also called the fully connected network (FCN). You learned how to build this network using Keras.

You also learned about the many tunable parameters that you need to tweak to get good results from your network. With Keras, a lot of the hard work has been done for you since it comes with sensible defaults, but there are occasions where this knowledge will be helpful to you.

Continuing on from there, you were introduced to...

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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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