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

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

By 作者: Thushan Ganegedara
ISBN-10 书号: 1788478312
ISBN-13 书号: 9781788478311
Release Finelybook 出版日期: 2018-05-31
pages 页数: 472

$39.99


Book Description to Finelybook sorting

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.
Contents
1: INTRODUCTION TO NATURAL LANGUAGE PROCESSING
2: UNDERSTANDING TENSORFLOW
3: WORD2VEC – LEARNING WORD EMBEDDINGS
4: ADVANCED WORD2VEC
5: SENTENCE CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS
6: RECURRENT NEURAL NETWORKS
7: LONG SHORT-TERM MEMORY NETWORKS
8: APPLICATIONS OF LSTM – GENERATING TEXT
9: APPLICATIONS OF LSTM – IMAGE CAPTION GENERATION
10: SEQUENCE-TO-SEQUENCE LEARNING – NEURAL MACHINE TRANSLATION
11: CURRENT TRENDS AND THE FUTURE OF NATURAL LANGUAGE PROCESSING
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
Authors
Thushan Ganegedara
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.

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Natural Language Processing with TensorFlow
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