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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

By : Saad, Ganegedara
4.5 (10)
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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

4.5 (10)
By: Saad, Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (14 chapters)
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13
Index

To get the most out of this book

To get the most out of this book, we assume the following from the reader:

  • A solid will and an ambition to learn the modern ways of NLP
  • Familiarity with basic Python syntax and data structures (for example, lists and dictionaries)
  • A good understanding of basic mathematics (for example, matrix/vector multiplication)
  • (Optional) Advance mathematics knowledge (for example, derivative calculation) to understand a handful of subsections that cover the details of how certain learning models overcome potential practical issues faced during training
  • (Optional) Read research papers to refer to advances/details in systems, beyond what the book covers

Download the example code files

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at http://www.packtpub.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of one of these:

  • WinRAR / 7-Zip for Windows
  • Zipeg / iZip / UnRarX for macOS
  • 7-Zip / PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Natural-Language-Processing-with-TensorFlow. 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://www.packtpub.com/sites/default/files/downloads/NaturalLanguageProcessingwithTensorFlow_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; "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

graph = tf.Graph() # Creates a graph
session = tf.InteractiveSession(graph=graph) # Creates a session

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

graph = tf.Graph() # Creates a graph
session = tf.InteractiveSession(graph=graph) # Creates a session

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

conda --version

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."

References: In Chapter 11, Current Trends and the Future of Natural Language Processing, in-text references include a bracketed number (for example, [1]) that correlates with the numbering in the References section at the end of the chapter.

Note

Warnings or important notes appear like this.

Tip

Tips and tricks appear like this.

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