There are many situations where we're interested in patterns demonstrating the co-occurrence of some items. For example, marketers want to know which goods are often bought together, clinical personnel need to know symptoms associated with certain medical conditions, and in information security we want to know which activity patterns are associated with intrusion or fraud. All of these problems have a common structure: there are items (goods, symptoms, records in logs) organized in transactions (shopping list, medical case, user activity transaction). With this type of data, we can then analyze it to find association rules, such as If the client bought a lemon and some cookies, he is also likely to buy tea, or in more formal notation: (cookies, lemon → tea).
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Machine Learning with Swift
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Machine Learning with Swift
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Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
Getting Started with Machine Learning
Classification – Decision Tree Learning
K-Nearest Neighbors Classifier
K-Means Clustering
Association Rule Learning
Linear Regression and Gradient Descent
Linear Classifier and Logistic Regression
Neural Networks
Convolutional Neural Networks
Natural Language Processing
Machine Learning Libraries
Optimizing Neural Networks for Mobile Devices
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