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

Chapter 8. Neural Networks

Just a decade ago, artificial neural networks (NNs) were considered by most researchers as an unpromising branch of computer science. But as computational power grew, and efficient algorithms to train NNs on GPUs were found, the situation changed dramatically. The latest discoveries in the field have achieved unprecedented results, such as tracking objects in video; synthesizing realistic speech, paintings, and music, automatic translation from one language to another; and extracting meaning from text, images, and video. NNs were rebranded as deep learning and they've set all kinds of records in computer vision and natural language processing, beating almost all other ML approaches over the last few years (2014-2018). Deep NNs caused a new machine learning boom, raising a wave of discussions and predictions about the artificial general intelligence forthcoming.

Now, there are already so many NN types that it's hard to keep track of them: convolutional, recurrent...