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
Other Books You May Enjoy
13
Index

Tensor/matrix operations

Transpose

Transpose is an important operation defined for matrices or tensors. For a matrix, the transpose is defined as follows:

Here, AT denotes the transpose of A.

An example of the transpose operation can be illustrated as follows:

After the transpose operation:

For a tensor, transpose can be seen as permuting the dimensions order. For example, let’s define a tensor S, as shown here:

Now one transpose operation (out of many) can be defined as follows:

Matrix multiplication

Matrix multiplication is another important operation that appears quite frequently in linear algebra.

Given the matrices and , the multiplication of A and B is defined as follows:

Here, .

Consider this example:

This gives , and the value of C is as follows:

Element-wise multiplication

Element-wise matrix multiplication (or the Hadamard product) is computed for two matrices...