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

Digit recognition

The digit recognition MNIST dataset was developed by Yann LeCun, Corinna Cortes, and Christopher Burges for assessing machine learning models on the handwritten digit problem. Digit images were taken from a mixture of scanned documents, normalized in size, and centered. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel value is an integer between 0 and 255, inclusive. We develop a digit recognition pipeline. We have 10 digits (0 to 9), or 10 classes, to predict.

Getting ready

In this recipe, we develop a modeling pipeline that tries to recognize a digit (0-9) based on images with greater accuracy. The modeling pipelines use CNN models written using the Keras functional API for image classification. 

The Keras library provides a simple method for loading the MNIST data. The dataset...