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

Classification for spam detection

Spam detection is a common classification problem. In the following recipe, we have the corpus of raw text or documents, including labels of those documents marked spam or no spam. The data source here is the SMS Spam Collection v.1, which is a public set of SMS labeled messages that have been collected for mobile phone spam research.


The dataset can be downloaded from The following table lists the provided dataset in different file formats, the number of samples in each class, and the total number of samples:

How to do it...

In this recipe, we develop a modeling pipeline for classification that tries to classify the spam type into ham or spam. The modeling pipelines...