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Produktbild: Machine Learning for iOS Developers

Machine Learning for iOS Developers

46,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.02.2020

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

23,1/19,3/2 cm

Gewicht

567 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-60287-3

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.02.2020

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

23,1/19,3/2 cm

Gewicht

567 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-60287-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Machine Learning for iOS Developers

  • Introduction xix

    Part 1 Fundamentals of Machine Learning 1

    Chapter 1 Introduction to Machine Learning 3

    What is Machine Learning? 4

    Tools Commonly Used by Data Scientists 4

    Common Terminology 5

    Real-World Applications of Machine Learning 7

    Types of Machine Learning Systems 8

    Supervised Learning 9

    Unsupervised Learning 10

    Semisupervised Learning 11

    Reinforcement Learning 11

    Batch Learning 12

    Incremental Learning 12

    Instance-Based Learning 13

    Model-Based Learning 13

    Common Machine Learning Algorithms 13

    Linear Regression 14

    Support Vector Machines 15

    Logistic Regression 19

    Decision Trees 21

    Artificial Neural Networks 23

    Sources of Machine Learning Datasets 24

    Scikit-learn Datasets 24

    AWS Public Datasets 27

    Kaggle.com Datasets 27

    UCI Machine Learning Repository 27

    Summary 28

    Chapter 2 The Machine-Learning Approach 29

    The Traditional Rule-Based Approach 29

    A Machine-Learning System 33

    Picking Input Features 34

    Preparing the Training and Test Set 39

    Picking a Machine-Learning Algorithm 40

    Evaluating Model Performance 41

    The Machine-Learning Process 44

    Data Collection and Preprocessing 44

    Preparation of Training, Test, and Validation Datasets 44

    Model Building 45

    Model Evaluation 45

    Model Tuning 45

    Model Deployment 46

    Summary 46

    Chapter 3 Data Exploration and Preprocessing 47

    Data Preprocessing Techniques 47

    Obtaining an Overview of the Data 47

    Handling Missing Values 57

    Creating New Features 60

    Transforming Numeric Features 62

    One-Hot Encoding Categorical Features 64

    Selecting Training Features 65

    Correlation 65

    Principal Component Analysis 68

    Recursive Feature Elimination 70

    Summary 71

    Chapter 4 Implementing Machine Learning on Mobile Apps 73

    Device-Based vs Server-Based Approaches 73

    Apple's Machine Learning Frameworks and Tools 75

    Task-Level Frameworks 75

    Model-Level Frameworks 76

    Format Converters 76

    Transfer Learning Tools 77

    Third-Party Machine-Learning Frameworks and Tools 78

    Summary 79

    Part 2 Machine Learning with CoreML, CreateML, and TuriCreate 81

    Chapter 5 Object Detection Using Pre- trained Models 83

    What is Object Detection? 83

    A Brief Introduction to Artificial Neural Networks 86

    Downloading the ResNet50 Model 92

    Creating the iOS Project 92

    Creating the User Interface 95

    Updating Privacy Settings 100

    Using the Resnet50 Model in the iOS Project 100

    Summary 109

    Chapter 6 Creating an Image Classifier with the Create ML App 111

    Introduction to the Create ML App 112

    Creating the Image Classification Model with the Create ML App 113

    Creating the iOS Project 117

    Creating the User Interface 118

    Updating Privacy Settings 122

    Using the Core ML Model in the iOS Project 123

    Summary 132

    Chapter 7 Creating a Tabular Classifier with Create ML 135

    Preparing the Dataset for the Create ML App 135

    Creating the Tabular Classification Model with the Create ML App 143

    Creating the iOS Project 147

    Creating the User Interface 148

    Using the Classification Model in the iOS Project 156

    Testing the App 172

    Summary 173

    Chapter 8 Creating a Decision Tree Classifier r 175

    Decision Tree Recap 175

    Examining the Dataset 176

    Creating Training and Test Datasets 180

    Creating the Decision Tree Classification Model with Scikit-learn 181

    Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 186

    Creating the iOS Project 187

    Creating the User Interface 188

    Using the Scikit-learn Decision Tree Classifier Model in the iOS Project 193

    Testing the App 201

    Summary 202

    Chapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML 203

    Examining the Dataset 203

    Creating a Training and Test Dataset 208

    Creating the Logistic Regression Model with Scikit-learn 210

    Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 216

    Creating the iOS Project 218

    Creating the User Interface 219

    Using the Scikit-learn Model in the iOS Project 225

    Testing the App 232

    Summary 233

    Chapter 10 Building a Deep Convolutional Neural Network with Keras 235

    Introduction to the Inception Family of Deep Convolutional Neural Networks 236

    GoogLeNet (aka Inception-v1) 236

    Inception-v2 and Inception-v3 238

    Inception-v4 and Inception-ResNet 239

    A Brief Introduction to Keras 244

    Implementing Inception-v4 with the Keras Functional API 246

    Training the Inception-v4 Model 259

    Exporting the Keras Inception-v4 Model to the Core ML Format 269

    Creating the iOS Project 270

    Creating the User Interface 271

    Updating Privacy Settings 276

    Using the Inception-v4 Model in the iOS Project 277

    Summary 286

    Appendix A Anaconda and Jupyter Notebook Setup 287

    Installing the Anaconda Distribution 287

    Creating a Conda Python Environment 288

    Installing Python Packages 291

    Installing Jupyter Notebook 293

    Summary 296

    Appendix B Introduction to NumPy and Pandas 297

    NumPy 297

    Creating NumPy Arrays 297

    Modifying Arrays 301

    Indexing and Slicing 304

    Pandas 305

    Creating Series and Dataframes 305

    Getting Dataframe Information 307

    Selecting Data 311

    Summary 313

    Index 315