We are very excited to announce that Zaggy AI has been accepted to the Microsoft for Startups Founders Hub! We look forward to working with the team at Founders Hub to explore the many services and solutions available in the Microsoft Azure ecosystem, as well as back office and productivity tools.
Similar Posts
Introducing Zaggy AI’s LapLabeler
We’re excited to introduce LapLabeler, a groundbreaking new tool specifically designed for labeling deep learning datasets in motor sports.
PashehNet v0.1.0 Released
Zaggy AI is proud to announce our first FOSS contribution, PashehNet. PashehNet is a tool for quickly and reproducibly creating simulated sensor networks (SSN) that can publish to a target system.
Comparing and Contrasting Supervised, Unsupervised, and Self-Supervised Deep Learning
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.
Sensor Fusion: An Overview
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.
Unsupervised and Self-Supervised Learning: The Future of AI
In the rapidly advancing field of artificial intelligence (AI), unsupervised and self-supervised learning is emerging as a transformative paradigm that promises to reshape the landscape.
Characterization, and why it matters in motorsports
By developing characterization approaches for drivers, vehicles, and tracks, we can better ascertain if each major component is playing its part to perfection.
