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E-raamat: Machine Learning for iOS Developers

  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119602910
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Machine Learning for iOS DevelopersSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119602910
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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.

Introduction xix
Part 1 Fundamentals of Machine Learning
1(80)
Chapter 1 Introduction to Machine Learning
3(26)
What Is Machine Learning?
4(4)
Tools Commonly Used by Data Scientists
4(1)
Common Terminology
5(2)
Real-World Applications of Machine Learning
7(1)
Types of Machine Learning Systems
8(5)
Supervised Learning
9(1)
Unsupervised Learning
10(1)
Semisupervised Learning
11(1)
Reinforcement Learning
11(1)
Batch Learning
12(1)
Incremental Learning
12(1)
Instance-Based Learning
13(1)
Model-Based Learning
13(1)
Common Machine Learning Algorithms
13(11)
Linear Regression
14(1)
Support Vector Machines
15(4)
Logistic Regression
19(2)
Decision Trees
21(2)
Artificial Neural Networks
23(1)
Sources of Machine Learning Datasets
24(4)
Scikit-learn Datasets
24(3)
AWS Public Datasets
27(1)
Kaggle.com Datasets
27(1)
UCI Machine Learning Repository
27(1)
Summary
28(1)
Chapter 2 The Machine-Learning Approach
29(18)
The Traditional Rule-Based Approach
29(4)
A Machine-Learning System
33(11)
Picking Input Features
34(5)
Preparing the Training and Test Set
39(1)
Picking a Machine-Learning Algorithm
40(1)
Evaluating Model Performance
41(3)
The Machine-Learning Process
44(2)
Data Collection and Preprocessing
44(1)
Preparation of Training, Test, and Validation Datasets
44(1)
Model Building
45(1)
Model Evaluation
45(1)
Model Tuning
45(1)
Model Deployment
46(1)
Summary
46(1)
Chapter 3 Data Exploration and Preprocessing
47(26)
Data Preprocessing Techniques
47(18)
Obtaining an Overview of the Data
47(10)
Handling Missing Values
57(3)
Creating New Features
60(2)
Transforming Numeric Features
62(2)
One-Hot Encoding Categorical Features
64(1)
Selecting Training Features
65(6)
Correlation
65(3)
Principal Component Analysis
68(2)
Recursive Feature Elimination
70(1)
Summary
71(2)
Chapter 4 Implementing Machine Learning on Mobile Apps
73(8)
Device-Based vs. Server-Based Approaches
73(2)
Apple's Machine Learning Frameworks and Tools
75(3)
Task-Level Frameworks
75(1)
Model-Level Frameworks
76(1)
Format Converters
76(1)
Transfer Learning Tools
77(1)
Third-Party Machine-Learning Frameworks and Tools
78(1)
Summary
79(2)
Part 2 Machine Learning with CoreML, CreateML, and TuriCreate
81(206)
Chapter 5 Object Detection Using Pre-trained Models
83(28)
What Is Object Detection?
83(3)
A Brief Introduction to Artificial Neural Networks
86(6)
Downloading the ResNet50 Model
92(1)
Creating the iOS Project
92(17)
Creating the User Interface
95(5)
Updating Privacy Settings
100(1)
Using the Resnet50 Model in the iOS Project
100(9)
Summary
109(2)
Chapter 6 Creating an Image Classifier with the Create ML App
111(24)
Introduction to the Create ML App
112(1)
Creating the Image Classification Model with the Create ML App
113(4)
Creating the iOS Project
117(15)
Creating the User Interface
118(4)
Updating Privacy Settings
122(1)
Using the Core ML Model in the iOS Project
123(9)
Summary
132(3)
Chapter 7 Creating a Tabular Classifier with Create ML
135(40)
Preparing the Dataset for the Create ML App
135(8)
Creating the Tabular Classification Model with the Create ML App
143(4)
Creating the iOS Project
147(26)
Creating the User Interface
148(8)
Using the Classification Model in the iOS Project
156(16)
Testing the App
172(1)
Summary
173(2)
Chapter 8 Creating a Decision Tree Classifier
175(28)
Decision Tree Recap
175(1)
Examining the Dataset
176(4)
Creating Training and Test Datasets
180(1)
Creating the Decision Tree Classification Model with Scikit-learn
181(5)
Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
186(1)
Creating the iOS Project
187(15)
Creating the User Interface
188(5)
Using the Scikit-learn DecisionTreeClassifier Model in the iOS Project
193(8)
Testing the App
201(1)
Summary
202(1)
Chapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML
203(32)
Examining the Dataset
203(5)
Creating a Training and Test Dataset
208(2)
Creating the Logistic Regression Model with Scikit-learn
210(6)
Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format
216(2)
Creating the iOS Project
218(15)
Creating the User Interface
219(6)
Using the Scikit-learn Model in the iOS Project
225(7)
Testing the App
232(1)
Summary
233(2)
Chapter 10 Building a Deep Convolutional Neural Network with Keras
235(52)
Introduction to the Inception Family of Deep Convolutional Neural Networks
236(8)
GoogLeNet (aka Inception-v1)
236(2)
Inception-v2 and Inception-v3
238(1)
Inception-v4 and Inception-ResNet
239(5)
A Brief Introduction to Keras
244(2)
Implementing Inception-v4 with the Keras Functional API
246(13)
Training the Inception-v4 Model
259(10)
Exporting the Keras Inception-v4 Model to the Core ML Format
269(1)
Creating the iOS Project
270(16)
Creating the User Interface
271(5)
Updating Privacy Settings
276(1)
Using the Inception-v4 Model in the iOS Project
277(9)
Summary
286(1)
Appendix A Anaconda and Jupyter Notebook Setup
287(10)
Installing the Anaconda Distribution
287(1)
Creating a Conda Python Environment
288(3)
Installing Python Packages
291(2)
Installing Jupyter Notebook
293(3)
Summary
296(1)
Appendix B Introduction to NumPy and Pandas
297(18)
NumPy
297(8)
Creating NumPy Arrays
297(4)
Modifying Arrays
301(3)
Indexing and Slicing
304(1)
Pandas
305(8)
Creating Series and Dataframes
305(2)
Getting Dataframe Information
307(4)
Selecting Data
311(2)
Summary
313(2)
Index 315
Abhishek Mishra has more than 19 years of experience across a broad range of mobile and enterprise technologies. He consults as a security and fraud solution architect with Lloyds Banking group PLC in London. He is the author of Machine Learning on the AWS Cloud, Amazon Web Services for Mobile Developers, iOS Code Testing, and Swift iOS: 24-Hour Trainer.
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