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 breast cancer

We will work on a problem of classification to predict whether a cancer is benign or malignant. We will drive through developing an algorithm that uses neural networks to accurately predict (~94 percent accuracy) if a breast cancer tumor is benign or malignant, basically teaching a machine to predict breast cancer. This may seem difficult, but using Keras APIs, we will have this seemingly complex model ready for use. The dataset description is provided in the following content:


Breast Cancer Wisconsin (Diagnostic) dataset:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at

Class distribution: 357 benign, 212 malignant.

This database is also available via the UW CS FTP server: ftp cd math-prog/cpo-dataset/machine-learn/WDBC/

How to do it...

In this recipe...