<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Networks on Daniel Bethell</title><link>https://daniel-bethell.co.uk/tags/neural-networks/</link><description>Recent content in Neural Networks on Daniel Bethell</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Daniel Bethell</copyright><lastBuildDate>Mon, 10 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://daniel-bethell.co.uk/tags/neural-networks/index.xml" rel="self" type="application/rss+xml"/><item><title>Visualising How Neural Networks Learn</title><link>https://daniel-bethell.co.uk/posts/visualising-embeddings/</link><pubDate>Mon, 10 Aug 2026 00:00:00 +0000</pubDate><guid>https://daniel-bethell.co.uk/posts/visualising-embeddings/</guid><description>Neural networks learn complex internal representations of their inputs, but these representations are usually difficult to inspect directly. In this post, I constrain an image classifier to a two-dimensional embedding space and interactively visualise how different training objectives organise that space over time.</description></item></channel></rss>