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

Python

Concepts

Mobile Application Development

Author (1):

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

To get the most out of this book

To get the most out of this book you need a basic understanding of TensorFlow or a similar framework such as PyTorch. Familiarity obtained through basic TensorFlow tutorials that are freely available in the web should suffice to get started on this book.

A basic knowledge of mathematics, including an understanding of n-dimensional tensors, matrix multiplication, and so on, will also prove invaluable throughout this book. Finally, you need an enthusiasm for learning about cutting edge machine learning that is setting the stage for modern NLP solutions.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/thushv89/packt_nlp_tensorflow_2. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781838641351_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “After running the pip install command, you should have Jupyter Notebook available in the Conda environment.”

A block of code is set as follows:

def layer(x, W, b):
    # Building the graph
    h = tf.nn.sigmoid(tf.matmul(x,W) + b) # Operation to perform
    return h

Any command-line input or output is written as follows:

<tf.Variable 'ref:0' shape=(3, 2) dtype=float32, numpy=
array([[-1., -9.],
       [ 3., 10.],
       [ 5., 11.]], dtype=float32)>

Bold: Indicates a new term or an important word. Words that you see on the screen (such as in menus or dialog boxes) also appear in the text like this , for example: “The feature that builds this computational graph automatically in TensorFlow is known as AutoGraph.”

Warnings or important notes appear like this.

Tips and tricks appear like this.