Book Image

Natural Language Processing with TensorFlow - Second Edition

By : Thushan Ganegedara
2 (1)
Book Image

Natural Language Processing with TensorFlow - Second Edition

2 (1)
By: Thushan Ganegedara

Overview of this book

Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Table of Contents (15 chapters)
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Introduction to Natural Language Processing

Natural Language Processing (NLP) offers a much-needed set of tools and algorithms for understanding and processing the large volume of unstructured data in today’s world. Recently, deep learning has been widely adopted for many NLP tasks because of the remarkable performance deep learning algorithms have shown in a plethora of challenging tasks, such as image classification, speech recognition, and realistic text generation. TensorFlow is one of the most intuitive and efficient deep learning frameworks currently in existence that enables such amazing feats. This book will enable aspiring deep learning developers to handle massive amounts of data using NLP and TensorFlow. This chapter covers the following topics:

  • What is Natural Language Processing?
  • Tasks of Natural Language Processing
  • The traditional approach to Natural Language Processing
  • The deep learning approach to Natural Language Processing
  • Introduction to the technical tools

In this chapter, we will provide an introduction to NLP and to the rest of the book. We will answer the question, “What is Natural Language Processing?”. Also, we’ll look at some of its most important use cases. We will also consider the traditional approaches and the more recent deep learning-based approaches to NLP, including a Fully Connected Neural Network (FCNN). Finally, we will conclude with an overview of the rest of the book and the technical tools we will be using.