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

Understanding Long Short-Term Memory Networks

In this section, we will first explain what happens within an LSTM cell. We will see that in addition to the states, a gating mechanism to control information flow inside the cell is present. Then we will work through a detailed example and see how each gate and states help at various stages of the example to achieve desired behaviors, finally leading to the desired output. Finally, we will compare an LSTM against a standard RNN to learn how an LSTM differs from a standard RNN.

What is an LSTM?

LSTMs can be seen as a fancier family of RNNs. An LSTM is composed mainly of five different things:

  • Cell state: This is the internal cell state (that is, memory) of an LSTM cell

  • Hidden state: This is the external hidden state used to calculate predictions

  • Input gate: This determines how much of the current input is read into the cell state

  • Forget gate: This determines how much of the previous cell state is sent into the current cell state

  • Output gate: This...