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Table Of Contents

Machine Learning with Core ML

5 (1)

Buy this Book

Machine Learning with Core ML

5 (1)

Buy this Book

Overview of this book

Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps. Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this book, you’ll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You’ll learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you’ll discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts. By the end of the book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Machine Learning

What is machine learning?

A brief tour of ML algorithms

A typical ML workflow

Summary

Introduction to Apple Core ML

Difference between training and inference

Inference on the edge

A brief introduction to Core ML

Learning algorithms

Considerations

Summary

Recognizing Objects in the World

Understanding images

Recognizing objects in the world

Performing inference

Summary

Emotion Detection with CNNs

Facial expressions

Input data and preprocessing

Bringing it all together

Summary

Locating Objects in the World

Object localization and object detection

Converting Keras Tiny YOLO to Core ML

Making it easier to find photos

Optimizing with batches

Summary

Creating Art with Style Transfer

Transferring style from one image to another

A faster way to transfer style

Converting a Keras model to Core ML

Building custom layers in Swift

Reducing your model's weight

Summary

Assisted Drawing with CNNs

Towards intelligent interfaces

Drawing

Recognizing the user's sketch

Summary

Assisted Drawing with RNNs

Assisted drawing

Recurrent Neural Networks for drawing classification

Input data and preprocessing

Bringing it all together

Summary

Object Segmentation Using CNNs

Classifying pixels

Data to drive the desired effect – action shots

Building the photo effects application

Working with probabilistic results

Summary

An Introduction to Create ML

A typical workflow

Preparing the data

Creating and training a model

Closing thoughts

Summary

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Recognizing Objects in the World

In this chapter, we will immerse ourselves in the world of machine learning (ML) and Core ML by working through what could be considered the 101 Core ML application. We will be using an image classification model to allow the user to point their iPhone at anything and have the app classify the most dominant object in the view.

We will start off by first discussing the concept of convolutional neural networks (ConvNets or CNNs), a category of neural networks well suited to image classification, before jumping into implementation. Starting from a skeleton project, you will soon discover just how easy it is to integrate ML into your apps with the help of Core ML.

In this chapter, we will cover the following topics: