Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

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
Index

Implementing an NMT from scratch – a German to English translator


Now we will implement an actual neural machine translator. We will be implementing the NMT using raw TensorFlow operations variables. The exercise is available in ch10/neural_machine_translation.ipynb. However, there is a sublibrary in TensorFlow, known as the seq2seq library. You can read more information about seq2seq as well as, learn to implement an NMT with seq2seq in the Appendix, Mathematical Foundations and Advanced TensorFlow.

The reason why we use raw TensorFlow is because, once you learn to implement a machine translator from scratch without using any helper functions, you will be able to quickly learn to use the seq2seq library. Furthermore, online resources are very scarce for learning to implement sequence-to-sequence models using raw TensorFlow. However, there are numerous resources/tutorials on how to use the seq2seq library for machine translation.

Note

TensorFlow provides very informative sequence to sequence...