In this chapter, we talked in detail about NMT systems. Machine translation is the task of translating a given text corpus from a source language to a target language. First we talked about the history of machine translation briefly to build a sense of appreciation for what has gone into machine translation, to become what it is today. We saw that today the highest performing machine translation systems are actually NMT systems. Next we talked about the fundamental concept of these systems and decomposed the model into the embedding layer, the encoder, the context vector, and the decoder. We first established the benefit of having an embedding layer as it gives semantic representations of words compared to one-hot-encoded vectors. Then we understood the objective of the encoder, which is to learn a good fixed dimensional vector that represents the source sentence. Next, once the fixed dimensional context vector was learned, we used this to initialize the decoder. The decoder is responsible...
Natural Language Processing with TensorFlow
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Natural Language Processing with TensorFlow
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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 (16 chapters)
Natural Language Processing with TensorFlow
Contributors
Preface
Free Chapter
Introduction to Natural Language Processing
Understanding TensorFlow
Word2vec – Learning Word Embeddings
Advanced Word2vec
Sentence Classification with Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Applications of LSTM – Generating Text
Applications of LSTM – Image Caption Generation
Sequence-to-Sequence Learning – Neural Machine Translation
Current Trends and the Future of Natural Language Processing
Mathematical Foundations and Advanced TensorFlow
Index
Customer Reviews