<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hyperparameter Tuning on jason grey</title><link>https://jason-grey.com/tags/hyperparameter-tuning/</link><description>Recent content in Hyperparameter Tuning on jason grey</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 17 May 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://jason-grey.com/tags/hyperparameter-tuning/index.xml" rel="self" type="application/rss+xml"/><item><title>Hyperparameter Tuning</title><link>https://jason-grey.com/posts/2023/hyperparameter-tuning/</link><pubDate>Wed, 17 May 2023 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2023/hyperparameter-tuning/</guid><description>&lt;p&gt;Doing some data science tonight. When it came time to tune my hyperparameters, I remembered I still had an account at &lt;a href="https://wandb.ai/site" class="external-link" target="_blank" rel="noopener"&gt;Weights &amp;amp; Biases&lt;/a&gt; and decided to give their &amp;ldquo;sweeps&amp;rdquo; feature a spin. &lt;a href="https://en.wikipedia.org/wiki/Hyperparameter_optimization" class="external-link" target="_blank" rel="noopener"&gt;Hyperparameter tuning&lt;/a&gt; is usually something I build into my notebooks/early scripts on a project and do it manually/simply. I have to say though, W&amp;amp;B made it pretty easy, their api is very easy to implement, is highly configurable, and has some pretty nice looking graphs to visualize what&amp;rsquo;s going on.&lt;/p&gt;</description></item></channel></rss>