<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Transformer on Batı Şengül</title>
    <link>http://www.batisengul.co.uk/tags/transformer/</link>
    <description>Recent content in Transformer on Batı Şengül</description>
    <generator>Hugo -- gohugo.io</generator>
    <managingEditor>batisengul@gmail.com</managingEditor>
    <webMaster>batisengul@gmail.com</webMaster>
    <lastBuildDate>Tue, 12 Mar 2019 00:00:00 +0000</lastBuildDate><atom:link href="http://www.batisengul.co.uk/tags/transformer/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>From Zero To State Of The Art NLP Part II - Transformers</title>
      <link>http://www.batisengul.co.uk/post/2019-03-12-from-zero-to-state-of-the-art-nlp-part-two/</link>
      <pubDate>Tue, 12 Mar 2019 00:00:00 +0000</pubDate>
      <author>batisengul@gmail.com</author>
      <guid>http://www.batisengul.co.uk/post/2019-03-12-from-zero-to-state-of-the-art-nlp-part-two/</guid>
      <description>&lt;div class=&#34;jupyter-cell markdown&#34;&gt;
&lt;p&gt;Welcome to part two of the two part series on a crash course into state of the art natural language processing. This part is going to go through the transformer architecture from &lt;a href=&#34;https://arxiv.org/abs/1706.03762&#34;&gt;Attention Is All You Need&lt;/a&gt;. If you haven&amp;rsquo;t done so already, read the &lt;a href=&#34;http://www.batisengul.co.uk/post/2019-03-06-from-zero-to-state-of-the-art-nlp-part-one/&#34;&gt;first part&lt;/a&gt; which introduces attention mechanisms. This post is all about transformers and assumes you know attention mechanisms.&lt;/p&gt;</description>
    </item>
    
  </channel>
</rss>
