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

Building a neural layer in Swift

A fully-connected layer is easy to implement, because it can be expressed as two operations:

  • A matrix multiplication between weights matrix W and input vector x.
  • A point wise application of activation function f :

Figure 8.5: One layer in detail

In many frameworks, the two operations are separated so that matrix multiplication happens in the fully-connected layer and activation happens in the next nonlinearity layer. This is handy because in this way we can easily replace the weighted sum with convolution. In the next chapter, we will discuss convolutional NNs.

But for now, let's see how NNs can perform logical operations. One neuron is enough to model any logical gate, except XOR. This finding caused the first AI winter in the 1960s; however, XOR is trivial to a model having a network with two layers.