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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Probability

Next, we will discuss the terminology related to probability theory. Probability theory is a vital part of machine learning, as modeling data with probabilistic models allows us to draw conclusions about how uncertain a model is about some predictions. Consider the example, where we performed sentiment analysis in Chapter 11, Current Trends and the Future of Natural Language Processing where we had an output value (positive/negative) for a given movie review. Though the model output some value between 0 and 1 (0 for negative and 1 for positive) for any sample we input, the model didn't know how uncertain it was about its answer.

Let's understand how uncertainty helps us to make better predictions. For example, a deterministic model might incorrectly say the positivity of the review, I never lost interest, is 0.25 (that is, more likely to be a negative comment). However, a probabilistic model will give a mean value and a standard deviation for the prediction. For example...

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