Leverage the power of machine learning and Swift programming to build intelligent iOS applications with ease
Key Features
- Implement effective machine learning solutions for your iOS applications
- Use Swift and Core ML to build and deploy popular machine learning models
- Develop neural networks for natural language processing and computer vision
Book Description
Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language.
We'll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development.
By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
What you will learn
- Learn rapid model prototyping with Python and Swift
- Deploy pre-trained models to iOS using Core ML
- Find hidden patterns in the data using unsupervised learning
- Get a deeper understanding of the clustering techniques
- Learn modern compact architectures of neural networks for iOS devices
- Train neural networks for image processing and natural language processing
Who This Book Is For
iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book.
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
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.
