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Machine Learning with Core ML : an IOS Developer's Guide to Implementing Machine Learning in Mobile Apps.
Discover the world of ML through the lens and application of Core ML. We will take you through examples; each example provides a new use case uncovering how ML can be applied specifically to computer vision tasks. By the end of the book, you will have the intuition and skills required to boost your...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: 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; Chapter 2: 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; Chapter 3: Recognizing Objects in the World; Understanding images; Recognizing objects in the world; Capturing data ; Preprocessing the data; Performing inference ; Summary ; Chapter 4: Emotion Detection with CNNs; Facial expressions; Input data and preprocessing ; Bringing it all together; Summary ; Chapter 5: 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.
- Chapter 6: Creating Art with Style TransferTransferring 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; Chapter 7: 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 ; Chapter 8: Assisted Drawing with RNNs; Assisted drawing.
- Recurrent Neural Networks for drawing classificationInput data and preprocessing ; Bringing it all together; Summary ; Chapter 9: 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; Chapter 10: 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; Index.