gaussian
![Brian Piercy](data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="%230A66C2"><path d="M20.447 20.452H16.89v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433a1.982 1.982 0 1 1 0-3.964 1.982 1.982 0 0 1 0 3.964zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>)
Understanding and coding Gaussian Splatting from a Python Engineer’s perspective
Understanding and coding how Gaussian’s are used within 3D Gaussian Splatting
Part 3 of our Gaussian Splatting tutorial, showing how to render splats onto a 2D image.
A more advanced clustering technique for real world data
Combining tree-boosting with Gaussian process and mixed effects models - fabsig/GPBoost
Improve clustering of user-item embedding by using GMM to generate new and tighter features
Gaussian Process Regression is a remarkably powerful class of machine learning algorithms. Here, we introduce them from first principles.
Understanding GMM: Idea, Maths, EM algorithm & python implementation
How to turn a collection of small building blocks into a versatile tool for solving regression problems.
Alex Bridgland blog/posts.