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


In this chapter, we looked at the implementations of the LSTM algorithm and other various important aspects to improve LSTMs beyond standard performance. As an exercise, we trained our LSTM on the text of stories by the Brothers Grimm and asked the LSTM to output a fresh new story. We discussed how to implement an LSTM with code examples extracted from exercises.

Next, we had a technical discussion about how to implement LSTMs with peepholes and GRUs. Then we did a performance comparison between a standard LSTM and its variants. We saw that the LSTMs performed the best compared to LSTMs with peepholes and GRUs. We made the surprising observation of peepholes actually hurting the performance rather than helping for our language modeling task.

Then we discussed some of the various improvements possible for enhancing the quality of outputs generated by an LSTM. The first improvement was beam search. We looked at an implementation of beam search and covered how to implement it step by...