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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow - Second Edition

By : Thushan Ganegedara
4.6 (17)
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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

4.6 (17)
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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12
Other Books You May Enjoy
13
Index

Transformers

Transformer models changed the playing field for most machine learning problems that involve sequential data. They have advanced the state of the art by a significant margin compared to the previous leaders, RNN-based models. One of the primary reasons that the Transformer model is so performant is that it has access to the whole sequence of items (e.g. sequence of tokens), as opposed to RNN-based models, which look at one item at a time. The term Transformer has come up several times in our conversations as a method that has outperformed other sequential models such as LSTMs and GRUs. Now, we will learn more about Transformer models.

In this chapter, we will first learn about the Transformer model in detail. Then we will discuss the details of a specific model from the Transformer family known as Bidirectional Encoder Representations from Transformers (BERT). We will see how we can use this model to complete a question-answering task.

Specifically, we will cover...

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Natural Language Processing with TensorFlow
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