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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
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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

Language modeling


The goal of language models is to compute a probability of a sequence of words. They are crucial to a lot of different applications, such as speech recognition, optical character recognition, machine translation, and spelling correction. For example, in American English, the two phrases wreck a nice beach and recognize speech are almost identical in pronunciation, but their respective meanings are completely different from each other. A good language model can distinguish which phrase is most likely correct, based on the context of the conversation. This section will provide an overview of word- and character-level language models and how RNNs can be used to build them.

Word-based models

A word-based language model defines a probability distribution over sequences of words. Given a sequence of words of length m, it assigns a probability P(w 1 , ... , w m ) to the full sequence of words. The application of these probabilities are two-fold. We can use them to estimate the...

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