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Machine Learning with Swift : Artificial Intelligence for iOS

Explore the high performance AI and Machine learning landscape for iOS platforms. About This Book * Implement Swift AI library, YCML, Machine Learning, Swift Brain, and more * Learn ML techniques from scratch, show implementation while building intelligent apps * Overview the whole spectrum of Apple numerical libraries and their hardware basis Who This Book Is For Developers who wish to learn the latest advancements in iOS for machine learning, and want to create intelligent iOS applications. Data Science professionals who are interested in knowing how to perform machine learning using in Swift will find this book useful. Familiarity with Swift programming and statistics is expected. What You Will Learn * Data collection and preparation. * Rapid Model prototyping in Swift playgrounds. * Detecting objects in images with Images classification techniques. * Feature extraction with Time series classification. * Clustering for user behaviour patterns detection. * Neural networks training, compression and deployment to iOS devices. * Implementing Chat bots, conversation and AI as an interface. In Detail Machine learning as a field promises to bring increasing intelligence to software's by helping learn and analyze information efficiently and discover certain things that humans cannot. This book is for iOS developers who dare to set foot on the trail to explore machine learning capabilities of iOS platform to build efficient, smart applications and avoid any unexpected pitfalls and traps. Just to mention several of them: giant models, noisy data, a whole zoo of libraries, and machine learning-native people speaking esoteric languages. We'll start from the very beginning in the first part of the book to develop lasting intuition about fundamental ml concepts. While we explore various supervised and unsupervised learning techniques in the second section, the third section will walk you through deep learning techniques with the help of common real-world cases, most popular AI tools and approaches for dealing with variety of data types. In the last section we'll dive into hardcore topics, like models compression and GPU acceleration and also provide some recommendation to avoid widespread mistakes during machine learning application development. By the end of the book, you'll have learned to develop intelligent applications that can learn for themselves written in Swift.
Product Details

Table of Contents

Table of Contents Getting started with Machine Learning Decision Tree Learning K-Neares Neighbor Classifier Clustering Rule learning Linear Regression and Gradient Descent Logistic Regression Neural Networks Convolutional Neural Networks and Computer Vision Word Embeddings and Natural Language Processing Machine Learning Libraries Optimizing neural networks for mobile devices Best Practices

About the Author

Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.

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