Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

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

Text summarization for reviews

We will work on the problem of text summarization to create relevant summaries for product reviews about fine food sold on the world's largest e-commerce platform, Amazon. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories. We develop a basic character-level sequence-to-sequence (seq2seq) model by defining an encoder-decoder recurrent neural network (RNN) architecture.

Our dataset includes the following:

  • 568,454 reviews
  • 256,059 users
  • 74,258 products


The dataset used in this recipe can be found at

How to do it…

In this recipe, we develop a modeling pipeline and encoder-decoder architecture that try to create relevant summaries for a given set of reviews. The modeling pipelines use RNN models written using the Keras functional API. The pipelines also use various data manipulation libraries.

The encoder-decoder architecture...