<?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>Deep Dives on Deep Dives &amp; Weekend Projects</title><link>https://blog.lukasmay.com/deep-dives/</link><description>Recent content in Deep Dives on Deep Dives &amp; Weekend Projects</description><generator>Hugo -- 0.161.1</generator><language>en-us</language><lastBuildDate>Fri, 06 Mar 2026 23:07:26 -0500</lastBuildDate><atom:link href="https://blog.lukasmay.com/deep-dives/index.xml" rel="self" type="application/rss+xml"/><item><title>Deep Learning</title><link>https://blog.lukasmay.com/deep-dives/deep-learning/</link><pubDate>Fri, 06 Mar 2026 23:07:26 -0500</pubDate><guid>https://blog.lukasmay.com/deep-dives/deep-learning/</guid><description>&lt;h2 id="intro"&gt;Intro&lt;/h2&gt;
&lt;p&gt;This is an attempt to cover what I know about DL to some degree. Some stuff is very skippable, and I don&amp;rsquo;t really remember everything that I put in here, so there might be some repeating, but not much.&lt;/p&gt;
&lt;h2 id="the-foundations-of-deep-learning"&gt;The Foundations of Deep Learning&lt;/h2&gt;
&lt;p&gt;To understand how machine learning models work, you have to completely discard the idea that it &amp;ldquo;understands&amp;rdquo; anything. A model does not read text or see images. At the absolute lowest level, a neural network is just a massively complex sequence of mathematical operations executed on silicon. To feed data into that silicon, we must first translate reality into a format that a GPU’s compute cores can process. That translation layer is the tensor.&lt;/p&gt;</description></item><item><title>AI Stack Part 1</title><link>https://blog.lukasmay.com/deep-dives/ai-stack-part-1/</link><pubDate>Mon, 16 Feb 2026 01:16:08 -0500</pubDate><guid>https://blog.lukasmay.com/deep-dives/ai-stack-part-1/</guid><description>&lt;h1 id="overview"&gt;Overview&lt;/h1&gt;
&lt;p&gt;I have been looking into self-hosting LLMs, and this is my attempt to put everything I&amp;rsquo;ve learned about the subject in one place (so I can stop forgetting things). Alongside that, I wanted to include information about the setup I use to self-host LLMs on my laptop and the steps I took to build and optimize it. While that will come in the future, as there are still some things I am changing, and this is long enough already, I removed some of those parts to put in the next section.&lt;/p&gt;</description></item></channel></rss>