Natural Language Processing with TensorFlow [ebook]

Natural Language Processing with TensorFlow

Updated on Mar, 2025

Programming, Data Science, Python

Formats : PDF (Read Only)

ISBN : 9781838647742

Edition : Second Edition

From introductory NLP tasks to Transformer models, this new edition teaches you to utilize powerful TensorFlow APIs to implement end-to-end NLP solutions driven by performant ML (Machine Learning) models

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.

Who this book is for

This book is for Python developers and programmers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks.

Fundamental Python skills are assumed, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required.

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