Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

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 (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

General-purpose machine learning libraries


In the following comparison tables, I have included around twenty libraries for machine learning. I considered such characteristics as the language of implementation and interface, the availability and type of acceleration, license type, ongoing development status, and compatibility with popular package managers. Later in this chapter, we will look at the unique features of each library in more detail.

Table 2.1: Comparison of general-purpose machine learning libraries for iOS (part 1):

Library

Language

Algorithms

AIToolbox

Swift

LinReg, LogReg, GMM, MDP, SVM, NN, PCA, k-means, genetic algorithms, DL: LSTM, CNN.

BrainCore

Swift

DL: FF, LSTM.

Caffe, Caffe2, MXNet, TensorFlow, tiny-dnn

C++

DL.

dlib

C++

Bayesian networks, SVMs, regressions, structured prediction, DL, clustering and other unsupervised, semi-supervised, reinforcement learning, feature selection.

FANN

C

NNs.

LearnKit

ObjC

Anomaly detection, collaborative filtering, decision trees, random forest, k-means, kNN...