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

Backpropagation Through Time


For training RNNs, a special form of backpropagation, known as Backpropagation Through Time (BPTT), is used. To understand BPTT, however, first we need to understand how backpropagation (BP) works. Then we will discuss why BP cannot be directly applied to RNNs, but how BP can be adapted to RNNs, resulting in BPTT. Finally, we will discuss two major problems present in BPTT.

How backpropagation works

Backpropagation is the technique that is used to train a feed-forward neural network. In backpropagation, you do the following:

  1. Calculate a prediction for a given input

  2. Calculate an error, E, of the prediction by comparing it to the actual label of the input (for example, mean squared error and cross-entropy loss)

  3. Update the weights of the feed-forward network to minimize the loss calculated in step 2, by taking a small step in the opposite direction of the gradient for all wij, where wij is the jth weight of ith layer

To understand more clearly, consider the feed-forward...