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Keras Deep Learning Cookbook

You're reading from Keras Deep Learning Cookbook

Product type Book

Published in Oct 2018

Publisher Packt

ISBN-13 9781788621755

Pages 252 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Rajdeep Dua

Sujit Pal

Manpreet Singh Ghotra

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Keras Installation

2. Working with Keras Datasets and Models

3. Data Preprocessing, Optimization, and Visualization

4. Classification Using Different Keras Layers

5. Implementing Convolutional Neural Networks

6. Generative Adversarial Networks

7. Recurrent Neural Networks

8. Natural Language Processing Using Keras Models

9. Text Summarization Using Keras Models

10. Reinforcement Learning

1. Other Books You May Enjoy

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Index

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...

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Authors (3)

Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

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Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

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Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

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