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Natural Language Processing with TensorFlow
Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

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Natural Language Processing with TensorFlow

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Key benefits

  • • Focuses on more efficient natural language processing using TensorFlow
  • • Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches
  • • Provides choices for how to process and evaluate large unstructured text datasets
  • • Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence

Description

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.

Who is this book for?

This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

What you will learn

  • • Core concepts of NLP and various approaches to natural language processing
  • • How to solve NLP tasks by applying TensorFlow functions to create neural networks
  • • Strategies to process large amounts of data into word representations that can be used by deep learning applications
  • • Techniques for performing sentence classification and language generation using CNNs and RNNs
  • • About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks
  • • How to write automatic translation programs and implement an actual neural machine translator from scratch
  • • The trends and innovations that are paving the future in NLP

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2018
Length: 472 pages
Edition : 1st
Language : English
ISBN-13 : 9781788477758
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Product Details

Publication date : May 31, 2018
Length: 472 pages
Edition : 1st
Language : English
ISBN-13 : 9781788477758
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Frequently bought together


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Total $ 131.97
Natural Language Processing and Computational Linguistics
$43.99
Natural Language Processing with TensorFlow
$43.99
Hands-On Natural Language Processing with Python
$43.99
Total $ 131.97 Stars icon

Table of Contents

13 Chapters
1. Introduction to Natural Language Processing Chevron down icon Chevron up icon
2. Understanding TensorFlow Chevron down icon Chevron up icon
3. Word2vec – Learning Word Embeddings Chevron down icon Chevron up icon
4. Advanced Word2vec Chevron down icon Chevron up icon
5. Sentence Classification with Convolutional Neural Networks Chevron down icon Chevron up icon
6. Recurrent Neural Networks Chevron down icon Chevron up icon
7. Long Short-Term Memory Networks Chevron down icon Chevron up icon
8. Applications of LSTM – Generating Text Chevron down icon Chevron up icon
9. Applications of LSTM – Image Caption Generation Chevron down icon Chevron up icon
10. Sequence-to-Sequence Learning – Neural Machine Translation Chevron down icon Chevron up icon
11. Current Trends and the Future of Natural Language Processing Chevron down icon Chevron up icon
A. Mathematical Foundations and Advanced TensorFlow Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(10 Ratings)
5 star 80%
4 star 10%
3 star 0%
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1 star 10%
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Victor Oct 07, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
With experience in Language Natural Processing I went into this book to introduce myself into TensorFlow. Definitely a good decision. The code is quite easy to follow, examples are useful and well explained. I recommend it.
Amazon Verified review Amazon
Deepal Bandaranayake Aug 27, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Natural language processing with Tensorflow is a very well-written book that gives a strong introduction to novel deep learning based NLP systems. With this book I've learned about word vectors, text generation, machine translation which are hot topics flying around at the moment.The book really dives into the details of implementing various NLP systems scanning through various TensorFlow functions involved in a modularized easy-to-follow manner. Each chapter is accompanied with Jupyter notebooks which again provide the full picture of the system end-to-end.If I'm to pick one particular example I liked in the book, I'd say it's the way the author describes the functioning of LSTMs. The author really brings the reader to his world and walk the reader through a easy-to-digest analogy of how an LSTM might operation, without much focus on mathematics. With that graceful entrance, he then continue to explain the LSTM in a mathematical perspective, which I found quite impressive.In conclusion, I genuinely enjoyed the book and think the book is a bang for bucks! I wouldn't hesitate this to another ML enthusiast looking for a good practical view of things!
Amazon Verified review Amazon
Tishan Jun 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book welcomes the reader to the modern deep learning based natural language processing techniques in a very progressive manner. Furthermore, the book often visualize elusive concepts with simpler explanations and colourful examples. For example, I liked the way Thushan discusses basics of TensorFlow and illustrates the workflow with a colourful example. Moreover, the book touches upon a multitude of NLP applications, providing a very diverse practical exposure to the current NLP solutions. I found the approach with more weight on the practicality and application, Thushan takes very appealing, in understanding the mechanics of various methods.
Amazon Verified review Amazon
Amazon Customer Sep 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The content covered in this book is excellent!!But binding quality of the publisher is very very poor. This happened for two books I purchased from Packet.
Amazon Verified review Amazon
Shirley Jun 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
it's useful~
Amazon Verified review Amazon
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