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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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Toc

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

Basic data structures

Scalar

A scalar is a single number unlike a matrix or a vector. For example, 1.3 is a scalar. A scalar can be mathematically denoted as follows: Scalar

Here, R is the real number space.

Vectors

A vector is an array of numbers. Unlike a set, where there is no order to elements, a vector has a certain order to the elements. An example vector is [1.0, 2.0, 1.4, 2.3]. Mathematically, it can be denoted as follows:

Vectors
Vectors

Alternatively, we can write this as:

Vectors

Here, R is the real number space and n is the number of elements in the vector.

Matrices

A matrix can be thought of as a two-dimensional arrangement of a collection of scalars. In other words, a matrix can be thought of as a vector of vectors. An example matrix would be as shown here:

Matrices

A more general matrix of size Matrices can be mathematically defined like this:

Matrices

And:

Matrices

Here, m is the number of rows of the matrix, n is the number of columns in the matrix, and R is the real number space.

Indexing of a matrix

We will be using zero-indexed notation...

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