<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gradients on Sjmelck</title><link>https://ryanbalshaw.github.io/sjmelck_pages/tags/gradients/</link><description>Recent content in Gradients on Sjmelck</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 16 Sep 2026 10:00:05 +0200</lastBuildDate><atom:link href="https://ryanbalshaw.github.io/sjmelck_pages/tags/gradients/index.xml" rel="self" type="application/rss+xml"/><item><title>An introduction to Radial basis functions</title><link>https://ryanbalshaw.github.io/sjmelck_pages/blog/rbf-models/</link><pubDate>Fri, 15 Mar 2024 11:59:05 +0200</pubDate><guid>https://ryanbalshaw.github.io/sjmelck_pages/blog/rbf-models/</guid><description>&lt;p&gt;Good day 👋&lt;/p&gt;&#10;&lt;p&gt;🧠 This is the first in a series of blog posts that deal with radial basis function surrogate models 🧑🏽‍🏫. The end goal will be to implement gradient enhanced models that complete powerful transformation procedures, but, we must first understand and implement the simplest version of these models.&lt;/p&gt;</description></item></channel></rss>