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

The machine learning pipeline for image caption generation


Here we will look at the image caption generation pipeline at a very high level and then discuss it piece by piece until we have the full model. The image caption generation framework consists of three main components and one optional component:

  • A CNN generating encoded vectors for images

  • An embedding layer learning word vectors

  • (Optional) An adaptation layer that can transform a given embedding dimensionality to an arbitrary dimensionality (details will be discussed later)

  • An LSTM taking the encoded vectors of the images, and outputting the corresponding caption

First, let's look at the CNN generating the encoded vectors for images. We can achieve this by first training a CNN on a large classification dataset, such as ImageNet, and using that knowledge to generate compressed vectorized representations of images.

One might ask, why not input the image as it is to the LSTM? Let's go back to a simple calculation we did in the previous chapter...