<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>PyTorch on Warpspace Blog</title><link>http://blog.caveduck.io/tags/pytorch/</link><description>Recent content in PyTorch on Warpspace Blog</description><generator>Hugo -- 0.154.5</generator><language>en-US</language><lastBuildDate>Mon, 27 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://blog.caveduck.io/tags/pytorch/index.xml" rel="self" type="application/rss+xml"/><item><title>Efficient ML #3 — Neural Net Pruning</title><link>http://blog.caveduck.io/posts/neural-net-pruning/</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/neural-net-pruning/</guid><description>Starting from LeCun&amp;rsquo;s 1989 Optimal Brain Damage, this is the story of shrinking a model by cutting weights away (pruning). What to cut, how, and how much; the surprise of compression that survives even a 95% cut thanks to retraining; and the point that you only get a real payoff when the hardware (NVIDIA 2:4) backs you up — all with plots we measured ourselves on an MNIST MLP.</description></item><item><title>Efficient ML #2 — Neural Net Quantization</title><link>http://blog.caveduck.io/posts/neural-net-quantization/</link><pubDate>Mon, 20 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/neural-net-quantization/</guid><description>We take the trained 32-bit weights of a neural net and shrink them down to 2–8 bits using two approaches: K-Means (non-uniform) and Linear (uniform, integer arithmetic). From the affine mapping r=S(q−Z) all the way to the compression-ratio vs. accuracy trade-off, with plots measured by actually running the code on an MNIST MLP.</description></item></channel></rss>