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Natural Language Processing with TensorFlow - Second Edition

You're reading from  Natural Language Processing with TensorFlow - Second Edition

Product type Book
Published in Jul 2022
Publisher Packt
ISBN-13 9781838641351
Pages 514 pages
Edition 2nd Edition
Languages
Author (1):
Thushan Ganegedara Thushan Ganegedara
Profile icon Thushan Ganegedara

Table of Contents (15) Chapters

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow 2 3. Word2vec – Learning Word Embeddings 4. Advanced Word Vector Algorithms 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Understanding Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Sequence-to-Sequence Learning – Neural Machine Translation 10. Transformers 11. Image Captioning with Transformers 12. Other Books You May Enjoy
13. Index
Appendix A: Mathematical Foundations and Advanced TensorFlow

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: .

Here, R is the real number space.

Vectors

A vector is an array of numbers. Unlike a set, where there is no order to the 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:

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 is shown as follows:

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

And:

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...

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