Generate AI Avatars yourself with Stable Diffusion 2.1 and Dreambooth
Learn how to generate your own unique avatars with Stable Diffusion and Dreambooth
Machine Learning
Automatically splitting an Audio File into smaller chunks based on silence
Learn how you can split an Audio File into smaller chunks to create an AI dataset
Train an Image Matting Model with Azure ML Custom Containers
Learn how you can train your own model through Azure Machine Learning and custom containers
Machine Teaching through Azure Project Bonsai - Series Overview
Autonomous Platforms are being deployed all around us, optimizing our daily processes and improving their efficiency and stability without the need of human intelligence. They learn by themself, iteratively improving themselves and staying ahead of the competition.
One of the biggest hurdles for Autonomous Platforms to be deployed is the
Azure Project Bonsai - Physical CartPole Project
In my previous post I revisited the Bonsai platform. Now it's time to actually start preparing to put it into production! (this will become more clearer over time 😉) To start off with a spoiler: Project Bonsai - It's impressive! 😎
Everything in this article is the result
The Azure Project Bonsai Platform Revisited
It has been almost an entire year ago since I have visited the Bonsai platform that I covered in my previous post. Since then a lot changed on the platform, so the time has come to revisit it and see in more detail what exactly changed (on the surface of
Gradually adopting Autonomous Systems in Production
Reinforcement Learning is something I have been covering a while now! Lately, I have seen this space growing tremendously but keep on seeing the same issue arising each time: "How can I as a company with an existing infrastructure / applications (= brownfield) adopt Reinfocement Learning?"
To answer this question,