Text summarization is a method in **natural language processing** (**NLP**) of generating a short and precise summary of a reference document. Producing a summary of a large document manually is a very difficult task. Summarization of a text using machine learning techniques is still an active research topic, and references for further reading are provided at the end of the chapter. Before proceeding to discuss text summarization and how we do it, we should define what a summary is. The summary is a text output that is generated from one or more texts that conveys relevant information from the original text in a shorter form. The goal of automatic text summarization is to transform the source text into a shorter version using semantics. Lately, various approaches have been developed for automated text summarization using NLP techniques, and they have been implemented widely in various domains. Some examples include search engines creating summaries for use in previews of documents and...

#### Keras Deep Learning Cookbook

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#### Keras Deep Learning Cookbook

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#### Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.
The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks.
By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning

Table of Contents (17 chapters)

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

Free Chapter

Keras Installation

Working with Keras Datasets and Models

Data Preprocessing, Optimization, and Visualization

Classification Using Different Keras Layers

Implementing Convolutional Neural Networks

Generative Adversarial Networks

Recurrent Neural Networks

Natural Language Processing Using Keras Models

Text Summarization Using Keras Models

Reinforcement Learning

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Index

Customer Reviews