Utilizing multi-core architecture in Python scripts could be challenging even though there is already a number of libraries for that. For simple cases the following home-brewed solution consisting of a small TaskManager class can be a viable option.
Here we look at the Pocket algorithm that addresses an important practical issue of PLA stability and the absence of convergence for non-separable training dataset.
Couple of 2d visualizations for Perceptron Learning Algorithm (PLA) are showing the behavior of plain vanilla PLA.
Generating artificial test data for Machine Learning (ML) algorithms is an important step in their development. This post discusses generation and plotting of linearly separable test data for binary classifiers like Perceptron.
Make sure to check out trueGeiger! This is an app for iOS devices to detect high energy radiation, co-developed with a friend of mine and available in app store.
The video explains how it works:
