<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on jason grey</title><link>https://jason-grey.com/tags/machine-learning/</link><description>Recent content in Machine Learning on jason grey</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 14 Jan 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://jason-grey.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Named in patent: Basketball training system with computer vision functionality</title><link>https://jason-grey.com/posts/2025/basketball-patent/</link><pubDate>Tue, 14 Jan 2025 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2025/basketball-patent/</guid><description>&lt;p&gt;A patent I am named on has made it through the machinery of he US Patent Office:&lt;/p&gt;
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 Basketball training system with computer vision functionality (US 12194357 Issued Jan 14, 2025)
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&lt;p&gt;A basketball training system that includes one or more of a basketball delivery machine, a computer vision sensor, one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations. The operations can include detecting a made or missed shot, identifying indexed video and/or pose information for the shot, tagging video and/or pose information with make or miss.&lt;/p&gt;</description></item><item><title>ML the ML - or, how to use ML to analyze the results of your hyperparameter tuning experiments (in Microsoft Fabric)</title><link>https://jason-grey.com/posts/2024/ml-the-ml/</link><pubDate>Sat, 06 Jan 2024 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2024/ml-the-ml/</guid><description>&lt;h1 id="what-are-we-talking-about-here"&gt;
 What are we talking about here?
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&lt;p&gt;When one is training a model, one typically engages in a process called &amp;ldquo;hyperparameter tuning.&amp;rdquo; The model is trained many (10s, 100s, or 1000&amp;rsquo;s of) times, varying some of the inputs. This could be as simple as the number of epochs, or, could be as varied as taking different slices or ranges of input data (ie: different sensors from an array of many sensors, etc), different ML model structures, or, different parameters within that structure.&lt;/p&gt;</description></item><item><title>AI for Natural Language Processing: Transforming Text Data into improving efficiency and engagement</title><link>https://jason-grey.com/posts/2023/ai-nlp-engangement/</link><pubDate>Fri, 01 Sep 2023 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2023/ai-nlp-engangement/</guid><description>&lt;p&gt;I spoke on Sept 7th. Recording of the session is here: &lt;a href="https://vimeo.com/event/3685788" class="external-link" target="_blank" rel="noopener"&gt;https://vimeo.com/event/3685788&lt;/a&gt;&lt;/p&gt;
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 AI for Natural Language Processing: Transforming Text Data into improving efficiency and engagement
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&lt;p&gt;Learn how AI-NLP techniques enable personalized user experiences, improve customer interactions, and drive higher engagement rates. From sentiment analysis to intelligent chatbots, we will showcase how AI-NLP powers efficient and engaging customer support. We will discuss the importance and implementation of guardrails in your model to ensure the returned results are accurate and on-topic.&lt;/p&gt;</description></item><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><item><title>Keep things simple presentation at Hasty.ai</title><link>https://jason-grey.com/posts/2022/keeping-things-simple-in-ml/</link><pubDate>Mon, 20 Feb 2023 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2022/keeping-things-simple-in-ml/</guid><description>&lt;p&gt;Hasty.ai / MLirl · Apr 7, 2022&lt;/p&gt;
&lt;p&gt;Learn what hands-on advice the speakers from our #MLirl event on Apr 7th had when getting started with AI projects.&lt;/p&gt;


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