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

Exercise – image classification on Fashion-MNIST with CNN

This will be our first example of using a CNN for a real-world machine learning task. We will classify images using a CNN. The reason for not starting with an NLP task is that applying CNNs to NLP tasks (for example, sentence classification) is not very straightforward. There are several tricks involved in using CNNs for such a task. However, originally, CNNs were designed to cope with image data. Therefore, let’s start there, and then find our way through to see how CNNs apply to NLP tasks in the Using CNNs for sentence classification section.

About the data

In this exercise, we will use a dataset well-known in the computer vision community: the Fashion-MNIST dataset. Fashion-MNIST was inspired by the famous MNIST dataset (http://yann.lecun.com/exdb/mnist/). MNIST is a database of labeled images of handwritten digits from 0 to 9 (i.e. 10 digits). However, due to the simplicity of the MNIST image classification...