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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

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Access this title in our online reader with advanced features

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Frequently bought together

Natural Language Processing with TensorFlow

$41.99

Transformers for Natural Language Processing

$89.99

$46.99

Total $ 178.97

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fatima fakih Sep 06, 2022

I literally like this book and I am in love with this book I would suggest everyone just read because I have actually passed my exam because of this book thanks Amazon

Amazon Verified review

hawkinflight Aug 06, 2022

I like that the book covers modern techniques such as Word2Vec, GloVe, ELMO, LSTMs, GRUs, NMT, and transformers/BERT architecture. The use-cases are interesting: 1)classifying sentences with CNNs 2)identifying named entities with RNNs 3)translation and chatbots using NMT and the attention mechanism 4)question and answer problem using transformers and BERT architecture 5)image captioning with transformers. I also like that the results are evaluated qualitatively and quantitatively and that metrics are proposed. I look forward to working with the code accompanying the book to try out the transformers. Huge thanks to the author - great book, great resource!

drei34 Mar 21, 2023

This book is fantastic for a number of reasons, not just bc of tensorflow code. For example, I don't use tf much and mostly do pytorch but I found quite a few topics explained here better than in papers or in "textbooks". One example: why do LSTMs solve the vanishing gradient problem better than RNN models? This book has some math derivations on this, you will not find that even in more hardcore (Goodfellow, etc) type books. A GREAT book - read it even if you know the material or do pytorch, you might find something new just in the math/examples not the code.

Placeholder Sep 06, 2022

The media could not be loaded. Thank you so much Amazon for giving me this book and this helps me a lot to pass the exam I have fully gone through by this book and other books as well but this was the best❤️❤️

Akshit shah Aug 10, 2022

This book covers all the areas of classic NLP - Word2vec, Word Vectors, CNNs, RNNs, Sequence-to-Sequence Learning, and of course Transformers There is enough explanation and comments in the code for me to follow along without getting lost. also, the results are evaluated qualitatively and quantitatively and metrics are proposed. I look forward to working with the code accompanying the book to try out the transformers. Huge thanks to the author - great book, great resource!

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

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