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 the network

When you first encounter a variety of architectures of CNNs, you feel overwhelmed by the abundance of new terms, different layers, and their hyperparameters. In fact, at the moment, only a few architectures have found broad application, and the number of designs suitable for mobile development is even smaller.

There are five basic types of layers plus an input layer, which usually does nothing except passing data forward:

  • Input layer: The first layer in the neural network. It does nothing, only takes the input and passes it downstream.
  • Convolution layers: Where convolutions happen
  • Fully: Connected or dense layers
  • Nonlinearity layers: These are layers which apply activation functions to the output of the previous layer: sigmoid, ReLU, tanh, softmax and so on.
  • Pooling layers: Downsample their input.
  • Regularization layers: layers to fight an overfitting.


Modern deep learning frameworks contain much more different types of layers for all needs, but these are the most commonly...