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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

Image Captioning with Transformers

Transformer models changed the playing field for many NLP problems. They have redefined the state of the art by a significant margin, compared to the previous leaders: RNN-based models. We have already studied Transformers and understood what makes them tick. Transformers have access to the whole sequence of items (e.g. a sequence of tokens), as opposed to RNN-based models that look at one item at a time, making them well-suited for sequential problems. Following their success in the field of NLP, researchers have successfully used Transformers to solve computer vision problems. Here we will learn how to use Transformers to solve a multi-modal problem involving both images and text: image captioning.

Automated image captioning, or image annotation, has a wide variety of applications. One of the most prominent applications is image retrieval in search engines. Automated image captioning can be used to retrieve all the images belonging to a certain...

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Natural Language Processing with TensorFlow
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