Title Page Copyright and Credits Machine Learning with Core ML Packt Upsell Why subscribe? 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 Other Books You May Enjoy Leave a review - let other readers know what you think