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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

Basic data structures

Scalar

A scalar is a single number, unlike a matrix or a vector. For example, 1.3 is a scalar. A scalar can be mathematically denoted as follows: .

Here, R is the real number space.

Vectors

A vector is an array of numbers. Unlike a set, where there is no order to the elements, a vector has a certain order to the elements. An example vector is [1.0, 2.0, 1.4, 2.3]. Mathematically, it can be denoted as follows:

Here, R is the real number space and n is the number of elements in the vector.

Matrices

A matrix can be thought of as a two-dimensional arrangement of a collection of scalars. In other words, a matrix can be thought of as a vector of vectors. An example matrix is shown as follows:

A more general matrix of size can be mathematically defined like this:

And:

Here, m is the number of rows of the matrix, n is the number of columns in the matrix, and R is the real number space.

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