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by Joshua Newnham
Machine Learning with Core ML
Title Page
Copyright and Credits
Machine Learning with Core ML
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introduction to Machine Learning
What is machine learning?
A brief tour of ML algorithms
Netflix – making recommendations 
Shadow draw – real-time user guidance for freehand drawing
Shutterstock – image search based on composition
iOS keyboard prediction – next letter prediction
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
Workflow 
Learning algorithms 
Auto insurance in Sweden
Supported learning algorithms
Considerations 
Summary
Recognizing Objects in the World
Understanding images
Recognizing objects in the world
Capturing data 
Preprocessing the data
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
Accelerating our layers 
Taking advantage of the GPU 
Reducing your model's weight
Summary
Assisted Drawing with CNNs
Towards intelligent interfaces 
Drawing
Recognizing the user's sketch
Reviewing the training data and model
Classifying sketches 
Sorting by visual similarity
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
Improving the model
Designing in constraints 
Embedding heuristics
Post-processing and ensemble techniques
Human assistance
Summary
An Introduction to Create ML
A typical workflow 
Preparing the data
Creating and training a model
Model parameters
Model metadata
Alternative workflow (graphical) 
Closing thoughts
Summary
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