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Machine Learning for Mobile

You're reading from  Machine Learning for Mobile

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Revathi Gopalakrishnan Revathi Gopalakrishnan
Profile icon Revathi Gopalakrishnan
Avinash Venkateswarlu Avinash Venkateswarlu
Profile icon Avinash Venkateswarlu
View More author details

Table of Contents (19) Chapters

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Introduction to Machine Learning on Mobile 2. Supervised and Unsupervised Learning Algorithms 3. Random Forest on iOS 4. TensorFlow Mobile in Android 5. Regression Using Core ML in iOS 6. The ML Kit SDK 7. Spam Message Detection 8. Fritz 9. Neural Networks on Mobile 10. Mobile Application Using Google Vision 11. The Future of ML on Mobile Applications 1. Question and Answers 2. Other Books You May Enjoy Index

Solving the problem


In this section, we are going to see a practical implementation of a neural network. We will define the problem statement, then we will understand the dataset we are going to use to solve the problem, whereupon we will create the model in Keras to solve the problem. Once the model is created in Keras, we will convert it into a model that's compatible with Core ML. This Core ML model will be imported into an iOS application, and a program will be written to use this model and interpret the handwritten digits.

Defining the problem statement

We are going to tackle the problem of recognizing handwritten digits through a machine learning model that we'll implement in an iOS mobile application. The first step is to have the database of handwritten digits that can be used for model training and testing.

The MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) provides a database of handwritten digits, and has a training set of 60,000 examples and a test set of 10,000 examples...

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