Machine learning with core ML : an ios developer's guide to implementing machine learning in mobile apps

Machine learning with core ML an ios developer's guide to implementing machine learning in mobile apps

Leverage the power of Apple's Core ML to create smart iOS apps About This Book Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Who This Book Is For Machine...

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Detalles Bibliográficos
Otros Autores: Newnham, Joshua, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, London ; Mumbai : Packt 2018.
Edición:1st edition
Materias:
iOS (Electronic resource)
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630399006719
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 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
  • 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 classification
  • Input 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.

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