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

Autoencoder neural networks

Autoencoder is a neural network whose goal is to produce an output identical to an input. For example, if you pass a picture into it, it should return the same picture on the other end. This seems... not complicated! But the trick is the special architecture—its inner layers have fewer neurons than input and output layers, usually with some extreme bottleneck in the middle. The layer before the bottleneck is called encoder and the layer after it is called decoder network. The encoder converts the input into some inner representation and the decoder then restores the data to its original form. During training, the network must figure out how to compress the input data most effectively and then un-compress it with the least possible information loss. This architecture can also be employed to train neural networks, which change input data in a way we want them to. For example, autoencoders have been successfully used to remove noise from images.


Autoencoder neural...