Machine Learning for iOS Developers

Name: Machine Learning for iOS Developers
Brand: Wiley
Price: 32.99 EUR
Availability: OnlineOnly

Abhishek Mishra(Autor*in)

Wiley (Verlag)

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Erschienen am 12. Februar 2020

352 Seiten

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978-1-119-60291-0 (ISBN)

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Beschreibung

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 Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications.

Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers:

* Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics

* Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming

* Develop skills in data acquisition and modeling, classification, and regression.

* Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS)

* Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML

Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.

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Inhalt

Cover
Title Page
Copyright
About the Author
About the Technical Editor
Acknowledgments
Contents at a Glance
Contents
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
Preparing the Dataset for the Create ML App
Creating the Tabular Classification Model with the Create ML App
Creating the iOS Project
Creating the User Interface
Using the Classification Model in the iOS Project
Testing the App
Summary
Chapter 8 Creating a Decision Tree Classifier
Decision Tree Recap
Examining the Dataset
Creating Training and Test Datasets
Creating the Decision Tree Classification Model with Scikit-learn
Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
Creating the iOS Project
Creating the User Interface
Using the Scikit-learn DecisionTreeClassifier Model in the iOS Project
Testing the App
Summary
Chapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML
Examining the Dataset
Creating a Training and Test Dataset
Creating the Logistic Regression Model with Scikit-learn
Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
Creating the iOS Project
Creating the User Interface
Using the Scikit-learn Model in the iOS Project
Testing the App
Summary
Chapter 10 Building a Deep Convolutional Neural Network with Keras
Introduction to the Inception Family of Deep Convolutional Neural Networks
GoogLeNet (aka Inception-v1)
Inception-v2 and Inception-v3
Inception-v4 and Inception-ResNet
A Brief Introduction to Keras
Implementing Inception-v4 with the Keras Functional API
Training the Inception-v4 Model
Exporting the Keras Inception-v4 Model to the Core ML Format
Creating the iOS Project
Creating the User Interface
Updating Privacy Settings
Using the Inception-v4 Model in the iOS Project
Summary
Appendix A Anaconda and Jupyter Notebook Setup
Installing the Anaconda Distribution
Creating a Conda Python Environment
Installing Python Packages
Installing Jupyter Notebook
Summary
Appendix B Introduction to NumPy and Pandas
NumPy
Creating NumPy Arrays
Modifying Arrays
Indexing and Slicing
Pandas
Creating Series and Dataframes
Getting Dataframe Information
Selecting Data
Summary
Index
EULA

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