SkyRadar Blog

In what follows we will present examples of Deep Learning networks and detail their various designs. It is a detailed tutorial, written for students and engineers who want to acquire a profound understanding of the subject.

In this tutorial, we will have a detailed look at one of the most powerful classes of machine learning and Artificial Intelligence algorithms that exists: the Bayesian Classifiers.

In this short video blog we compare a fixed threshold with the more versatile C-FAR algorithm.

In the first part of this series, we introduced the general concepts needed for understanding the Hidden Markov Models Classifiers. Namely: Bayesian Logic, The concept of Bayesian Classifiers and Bayesian networks. All these notions are now grouped to form a new type of classifier which can accurately model and classify time-series data such as the RADAR data.

In this article we will explore a very special class of classifiers, the Hidden Markov Model Classifiers (HMMs). They are mainly a statistical and probabilistic model but they have found their entry in the world of Machine Learning since they can be trained and classify data.

A pilot is getting ready to depart for a particular destination. Before departure, he or she will plan his flight just like any traveler who plans about his journey.

In air traffic services, two protagonists hold a significant position: Pilots and Air Traffic Controllers. In Air Traffic Services pilots are being instructed and advised by air traffic controllers at almost frequent intervals.

In this article we shall perform the anatomy of a simple but efficient convolutional network (CNN), the LeNet-1 neural network.