Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

Using word embeddings for spam detection

Because of the widespread availability of various robust embeddings generated from large corpora, it has become quite common to use one of these embeddings to convert text input for use with machine learning models. Text is treated as a sequence of tokens. The embedding provides a dense fixed dimension vector for each token. Each token is replaced with its vector, and this converts the sequence of text into a matrix of examples, each of which has a fixed number of features corresponding to the dimensionality of the embedding.

This matrix of examples can be used directly as input to standard (non-neural network based) machine learning programs, but since this book is about deep learning and TensorFlow, we will demonstrate its use with a one-dimensional version of the Convolutional Neural Network (CNN) that you learned about in Chapter 4, Convolutional Neural Networks. Our example is a spam detector that will classify Short Message Service...