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

Training the network


The most common way to train NNs these days is with a backward propagation of errors algorithm, or backpropagation (often backprop for short). As we have seen already, individual neurons remind us of linear or logistic regression a lot, so it should not come as a surprise that backpropagation usually comes together with our old friend the gradient descent algorithm. NN training works in the following way:

  • Forward pass—input is presented to the layer and the transformations are applied to it layer by layer until the prediction is outputted on the last layer.
  • Loss computation—the prediction is compared to the ground truth, and an error value is calculated for each neuron of the output layer using the loss function J.
  • The errors are then propagated backward (backpropagation), such that each neuron has an error associated to it, proportional to its contribution to the output.
  • Weights (w) are updated using one step of gradient descent. The gradient of the loss function 
     is calculated...