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Title Page
Copyright and Credits
Machine Learning with Core ML
Packt Upsell
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PacktPub.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
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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