Zaggy AI is now a part of Microsoft for Startups Founders Hub

Deep learning, a subset of machine learning, has taken the technological world by storm, underpinning the advancements in various applications from autonomous vehicles to drug discovery. Three dominant paradigms within deep learning are supervised, unsupervised, and self-supervised learning. In this article, we will elucidate these methods, noting their similarities and distinctions.

Nothing too crazy to report in this patch-level release: Latest package has been published on PyPi and latest docs are up. Let us know if you have any issues or feature requests on our GitHub project page!

Over 300 sensors and 1.5TB of data per car over the weekend, and they had to wait ’til post-race to analyze the data because of time and manpower constraints at the track. The concept for RAICE was born.

Sensor fusion, in its essence, is the combination of sensory data from diverse sources to generate a comprehensive understanding of an environment. This data integration process seeks to produce more consistent, accurate, and useful information than would be possible by relying on a single sensor alone. By merging information from various sensors, sensor fusion can address individual sensor limitations like noise, inaccuracies, or failure.

We’re excited to introduce LapLabeler, a groundbreaking new tool specifically designed for labeling deep learning datasets in motor sports.

By developing characterization approaches for drivers, vehicles, and tracks, we can better ascertain if each major component is playing its part to perfection.