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)
12
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13
Index

Understanding TensorFlow 2

In this chapter, you will get an in-depth understanding of TensorFlow. This is an open source distributed numerical computation framework, and it will be the main platform on which we will be implementing all our exercises. This chapter covers the following topics:

  • What is TensorFlow?
  • The building blocks of TensorFlow (for example, variables and operations)
  • Using Keras for building models
  • Implementing our first neural network

We will get started with TensorFlow by defining a simple calculation and trying to compute it using TensorFlow. After we complete this, we will investigate how TensorFlow executes this computation. This will help us to understand how the framework creates a computational graph to compute the outputs and execute this graph to obtain the desired outputs. Then we will dive into the details of how TensorFlow architecture operates by looking at how TensorFlow executes things, with the help of an analogy...