<?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>Transformer on Yeongseung's Blog</title><link>https://yeongseung.github.io/tags/transformer/</link><description>Recent content in Transformer on Yeongseung's Blog</description><generator>Hugo -- 0.160.1</generator><language>en</language><lastBuildDate>Tue, 24 Feb 2026 19:51:23 +0900</lastBuildDate><atom:link href="https://yeongseung.github.io/tags/transformer/index.xml" rel="self" type="application/rss+xml"/><item><title>Understanding and Building the Transformer</title><link>https://yeongseung.github.io/posts/transformer/post/</link><pubDate>Tue, 24 Feb 2026 19:51:23 +0900</pubDate><guid>https://yeongseung.github.io/posts/transformer/post/</guid><description>&lt;h1 id="introduction"&gt;Introduction&lt;/h1&gt;
&lt;p&gt;The transformer architecture has become the foundation of modern deep learning, powering everything from language models to vision models. Since I&amp;rsquo;ll be referencing transformers frequently in future posts, I wanted to create my own comprehensive analysis of the architecture. In this post, I&amp;rsquo;ll walk through building a transformer-based translation model step by step, following the original paper by Vaswani et al. (2017)&lt;sup id="fnref:1"&gt;&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref"&gt;1&lt;/a&gt;&lt;/sup&gt;. We&amp;rsquo;ll dive into the code, understanding each component as we implement it.&lt;/p&gt;</description></item></channel></rss>