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Machine Learning with Swift : Artificial Intelligence for iOS.

Author: Oleksandr Sosnovshchenko; Oleksandr Baiev
Publisher: Birmingham : Packt Publishing, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Machine learning has become a hot topic for developers who want to impart intelligent functionality to their applications. In this book, we'll show you how to incorporate various machine learning libraries available for iOS developers. You'll quickly get acquainted with the machine learning fundamentals and implement various algorithms with Swift.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Sosnovshchenko, Oleksandr.
Machine Learning with Swift : Artificial Intelligence for iOS.
Birmingham : Packt Publishing, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Oleksandr Sosnovshchenko; Oleksandr Baiev
ISBN: 9781787123526 1787123529 9781787121515 1787121518
OCLC Number: 1028181695
Notes: Chapter 5: Association Rule Learning.
Description: 1 online resource (371 pages)
Contents: Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Machine Learning; What is AI?; The motivation behind ML; What is ML?; Applications of ML; Digital signal processing (DSP); Computer vision; Natural language processing (NLP); Other applications of ML; Using ML to build smarter iOS applications; Getting to know your data; Features; Types of features; Choosing a good set of features; Getting the dataset; Data preprocessing; Choosing a model; Types of ML algorithms; Supervised learning; Unsupervised learning. Reinforcement learningMathematical optimization â#x80;#x93; how learning works; Mobile versus server-side ML; Understanding mobile platform limitations; Summary; Bibliography; Chapter 2: Classification â#x80;#x93; Decision Tree Learning; Machine learning toolbox; Prototyping the first machine learning app; Tools; Setting up a machine learning environment; IPython notebook crash course; Time to practice; Machine learning for extra-terrestrial life explorers; Loading the dataset; Exploratory data analysis; Data preprocessing; Converting categorical variables; Separating features from labels; One-hot encoding. Splitting the dataDecision trees everywhere; Training the decision tree classifier; Tree visualization; Making predictions; Evaluating accuracy; Tuning hyperparameters; Understanding model capacity trade-offs; How decision tree learning works; Building a tree automatically from data; Combinatorial entropy; Evaluating performance of the model with data; Precision, recall, and F1-score; K-fold cross-validation; Confusion matrix; Implementing first machine learning app in Swift; Introducing Core ML; Core ML features; Exporting the model for iOS; Ensemble learning random forest. Training the random forestRandom forest accuracy evaluation; Importing the Core ML model into an iOS project; Evaluating performance of the model on iOS; Calculating the confusion matrix; Decision tree learning pros and cons; Summary; Chapter 3: K-Nearest Neighbors Classifier; Calculating the distance; DTW; Implementing DTW in Swift; Using instance-based models for classification and clustering; People motion recognition using inertial sensors; Understanding the KNN algorithm; Implementing KNN in Swift; Recognizing human motion using KNN; Cold start problem; Balanced dataset. Choosing a good kReasoning in high-dimensional spaces; KNN pros; KNN cons; Improving our solution; Probabilistic interpretation; More data sources; Smarter time series chunking; Hardware acceleration; Trees to speed up the inference; Utilizing state transitions; Summary; Bibliography; Chapter 4: K-Means Clustering; Unsupervised learning; K-means clustering; Implementing k-means in Swift; Update step; Assignment step; Clustering objects on a map; Choosing the number of clusters; K-means clustering â#x80;#x93; problems; K-means++; Image segmentation using k-means; Summary.

Abstract:

Machine learning has become a hot topic for developers who want to impart intelligent functionality to their applications. In this book, we'll show you how to incorporate various machine learning  Read more...

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