This is the 5th article in our MAFAT Radar competition series, where we take an in-depth look at the different aspects of the challenge and our approach to it. If you want a recap, check out previous posts: the introduction, the dataset, augmentations, and visualizing signal data.

When dealing with a Supervised Machine Learning task one of the most important things you need is data — and lots of it. What does one do when faced with a small amount of data, especially for a task that requires Deep Neural Networks? …

This is the 2nd article in our MAFAT Radar competition series, where we take an in-depth look at the different aspects of the challenge and our approach to it. If you want a recap, check out this post.

Let’s jump straight in.

The competition organizers give a clear explanation of the data they provide:

The dataset consists of signals recorded by ground doppler-pulse radars. Each radar “stares” at a fixed, wide area of interest. Whenever an animal or a human moves within the radar’s covered area, it is detected and tracked. The dataset contains records of those tracks. The tracks…

Participating in Data Science competitions is one of the main ways Data Scientists can get experience working with “real world” datasets without working professionally in the field. Winning a competition is a huge achievement and can be the ticket to lucrative offers from top AI companies. Starting with competitions is often recommended for entry-level Data Scientists and students to build experience with the craft of Machine Learning.

Kaggle is the most popular and well known of these platforms, and since launching in 2010 it has exploded in popularity alongside the of ML and Data Science boom. …