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Python Deep Learning

You're reading from   Python Deep Learning Next generation techniques to revolutionize computer vision, AI, speech and data analysis

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786464453
Length 406 pages
Edition 1st Edition
Languages
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Authors (4):
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Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning – An Introduction FREE CHAPTER 2. Neural Networks 3. Deep Learning Fundamentals 4. Unsupervised Feature Learning 5. Image Recognition 6. Recurrent Neural Networks and Language Models 7. Deep Learning for Board Games 8. Deep Learning for Computer Games 9. Anomaly Detection 10. Building a Production-Ready Intrusion Detection System Index

Deep learning applications

In the next couple of paragraphs, we will discuss how deep neural networks have applications in the field of speech recognition and computer vision, and how their application in recent years has vastly improved accuracy in these two fields by completely outperforming many other machine learning algorithms not based on deep neural networks.

Speech recognition

Deep learning has started to be used in speech recognition starting in this decade (2010 and later, see for example the 2012 article titled Deep Neural Networks for Acoustic Modeling in Speech Recognition by Hinton et al., available online at http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/38131.pdf); until then, speech recognition methods were dominated by algorithms called GMM-HMM methods (Hidden Markov Models with Gaussian Mixture Emission). Understanding speech is a complex task, since speech is not, as is naively thought, made up of separate words with clear boundaries between...

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