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

by Thushan Ganegedara

Overview

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.

Product Details

ISBN-13:	9781788477758
Publisher:	Packt Publishing
Publication date:	05/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	472
File size:	22 MB
Note:	This product may take a few minutes to download.

About the Author

Thushan Ganegedara is currently a third year Ph.D. student at the University of Sydney, Australia. He is specializing in machine learning and has a liking for deep learning. He lives dangerously and runs algorithms on untested data. He also works as the chief data scientist for AssessThreat, an Australian start-up. He got his BSc. (Hons) from the University of Moratuwa, Sri Lanka. He frequently writes technical articles and tutorials about machine learning. Additionally, he also strives for a healthy lifestyle by including swimming in his daily schedule.

Table of Contents

Table of Contents

1. Introduction
2. How to Get TensorFlow to Work
3. Producing Word Embeddings with Word2Vec
4. Advanced Word2Vec
5. Sentence Classification with CNNs
6. Language Modelling with RNNs
7. What is LSTM?
8. Applying LSTM to Text Generation
9. Applications of LSTM: Image Caption Generation
10. Neural Machine Translation
11. NLP developments and Trends
12. Appendix I Linear Algebra and Statistics