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

Understanding Recurrent Neural Networks


In this section, we will discuss what an RNN is by starting with a gentle introduction, and then move on to more in-depth technical details. We mentioned earlier that RNNs maintain a state variable which evolves over time as the RNN is seeing more data, thus giving the power to model sequential data. In particular, this state variable is updated over time by a set of recurrent connections. Existence of recurrent connections is the main structural difference between an RNN and a feed-forward network. The recurrent connections can be understood as links between a series of memory RNN learned in the past, connecting to the current state variable of the RNN. In other words, the recurrent connections update the current state variable with respect to the past memory the RNN has, enabling the RNN to make a prediction based on the current input as well as the previous inputs.

In the upcoming section, we will discuss the following things. First, we will discuss...