Machine learning for iOS developers /

Mostrar otras versiones (1)

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial In...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mishra, Abhishek (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley And Sons, Inc, 2020.
Temas:
iOS (Electronic resource)
Machine learning.
Computers.
Computers
Machine Learning
Apprentissage automatique.
Ordinateurs.
computers.
COMPUTERS > Machine Theory.
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Introduction
  • What Does This Book Cover?
  • Additional Resources
  • Reader Support for This Book
  • Part 1 Fundamentals of Machine Learning
  • Chapter 1 Introduction to Machine Learning
  • What Is Machine Learning?
  • Tools Commonly Used by Data Scientists
  • Common Terminology
  • Real-World Applications of Machine Learning
  • Types of Machine Learning Systems
  • Supervised Learning
  • Unsupervised Learning
  • Semisupervised Learning
  • Reinforcement Learning
  • Batch Learning
  • Incremental Learning
  • Instance-Based Learning
  • Model-Based Learning
  • Common Machine Learning Algorithms
  • Linear Regression
  • Support Vector Machines
  • Logistic Regression
  • Decision Trees
  • Artificial Neural Networks
  • Sources of Machine Learning Datasets
  • Scikit-learn Datasets
  • AWS Public Datasets
  • Kaggle.com Datasets
  • UCI Machine Learning Repository
  • Summary
  • Chapter 2 The Machine-Learning Approach
  • The Traditional Rule-Based Approach
  • A Machine-Learning System
  • Picking Input Features
  • Preparing the Training and Test Set
  • Picking a Machine-Learning Algorithm
  • Evaluating Model Performance
  • The Machine-Learning Process
  • Data Collection and Preprocessing
  • Preparation of Training, Test, and Validation Datasets
  • Model Building
  • Model Evaluation
  • Model Tuning
  • Model Deployment
  • Summary
  • Chapter 3 Data Exploration and Preprocessing
  • Data Preprocessing Techniques
  • Obtaining an Overview of the Data
  • Handling Missing Values
  • Creating New Features
  • Transforming Numeric Features
  • One-Hot Encoding Categorical Features
  • Selecting Training Features
  • Correlation
  • Principal Component Analysis
  • Recursive Feature Elimination
  • Summary
  • Chapter 4 Implementing Machine Learning on Mobile Apps
  • Device-Based vs. Server-Based Approaches
  • Apple's Machine Learning Frameworks and Tools
  • Task-Level Frameworks
  • Model-Level Frameworks
  • Format Converters
  • Transfer Learning Tools
  • Third-Party Machine-Learning Frameworks and Tools
  • Summary
  • Part 2 Machine Learning with CoreML, CreateML, and TuriCreate
  • Chapter 5 Object Detection Using Pre-trained Models
  • What Is Object Detection?
  • A Brief Introduction to Artificial Neural Networks
  • Downloading the ResNet50 Model
  • Creating the iOS Project
  • Creating the User Interface
  • Updating Privacy Settings
  • Using the Resnet50 Model in the iOS Project
  • Summary
  • Chapter 6 Creating an Image Classifier with the Create ML App
  • Introduction to the Create ML App
  • Creating the Image Classification Model with the Create ML App
  • Creating the iOS Project
  • Creating the User Interface
  • Updating Privacy Settings
  • Using the Core ML Model in the iOS Project
  • Summary
  • Chapter 7 Creating a Tabular Classifier with Create ML

Ejemplares similares