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

Applications of RNNs

So far what we have talked about is a one-to-one mapped RNN, where the current output depends on the current input as well as the previously observed history of inputs. This means that there exists an output for the sequence of previously observed inputs and the current input. However, in the real word, there can be situations where there is only one output for a sequence of inputs, a sequence of outputs for a single input, and a sequence of outputs for a sequence of inputs where the sequence sizes are different. In this section, we will look at a few such applications.

One-to-one RNNs

In one-to-one RNNs, the current input depends on the previously observed inputs (see Figure 6.8). Such RNNs are appropriate for problems where each input has an output, but the output depends both on the current input and the history of inputs that led to the current input. An example of such a task is stock market prediction, where we output a value for the current input, and this output...