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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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Index

Sentence Classification with Convolutional Neural Networks

In this chapter, we will discuss a type of neural network known as Convolutional Neural Networks (CNNs). CNNs are quite different from fully connected neural networks and have achieved state-of-the-art performance in numerous tasks. These tasks include image classification, object detection, speech recognition, and of course, sentence classification. One of the main advantages of CNNs is that, compared to a fully connected layer, a convolution layer in a CNN has a much smaller number of parameters. This allows us to build deeper models without worrying about memory overflow. Also, deeper models usually lead to better performance.

We will introduce you to what a CNN is in detail by discussing different components found in a CNN and what makes CNNs different from their fully connected counterparts. Then we will discuss the various operations used in CNNs, such as the convolution and pooling operations, and certain hyperparameters...

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Natural Language Processing with TensorFlow
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