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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 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

Keras 2.0 — what is new

WaveNet — a generative model for learning how to produce audio

WaveNet is a deep generative model for producing raw audio waveforms. This breakthrough technology was introduced (https://deepmind.com/blog/wavenet-generative-model-raw-audio/) by Google DeepMind (https://deepmind.com/) for teaching users how to speak to computers. The results are truly impressive, and you can find online examples of synthetic voices where the computer learns how to talk with the voices of celebrities such as Matt Damon. So, you might wonder why learning to synthesize audio is so difficult. Well, each digital sound we hear is based on 16,000 samples per second (sometimes, 48,000 or more), and building a predictive model where we learn to reproduce a sample based on all the previous ones is a very difficult challenge. Nevertheless, there are experiments showing that WaveNet has improved current state-of-the-art text-to-speech (TTS) systems, reducing the difference with human voices by 50% for both US English...

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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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