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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781838641351
Length 514 pages
Edition 2nd Edition
Languages
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Author (1):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 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

Tensor/matrix operations

Transpose

Transpose is an important operation defined for matrices or tensors. For a matrix, the transpose is defined as follows:

Here, AT denotes the transpose of A.

An example of the transpose operation can be illustrated as follows:

After the transpose operation:

For a tensor, transpose can be seen as permuting the dimensions order. For example, let’s define a tensor S, as shown here:

Now one transpose operation (out of many) can be defined as follows:

Matrix multiplication

Matrix multiplication is another important operation that appears quite frequently in linear algebra.

Given the matrices and , the multiplication of A and B is defined as follows:

Here, .

Consider this example:

This gives , and the value of C is as follows:

Element-wise multiplication

Element-wise matrix multiplication (or the Hadamard product) is computed for two matrices...

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