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

Cervical cancer classification

Cervical cancer is cancer that occurs in the cervix. Cervical cancer is easy to counter if caught in its early stages. However, due to lack of expertise in the field, one of the biggest challenges for cervical cancer screening and treatment programs is determining a suitable method of treatment. The treatment workflow would be greatly improved given the ability to make real-time determinations about a patients treatment eligibility based on cervix type.

Getting ready

In this recipe, we develop a modeling pipeline that tries to identify a woman's cervix type based on images with greater accuracy. The modeling pipeline uses a CNN models written using the Keras functional API for image classification. The pipeline also use a various image manipulation libraries.


The data for this recipe can be found at The dataset is part of the challenge to develop an algorithm that accurately identifies a woman...