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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Aditya Nori</author_name><author_url>https://www.microsoft.com/en-us/research/people/adityan/</author_url><title>Debugging Machine Learning Tasks - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="zY10xfWUOx"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/debugging-machine-learning-tasks/"&gt;Debugging Machine Learning Tasks&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/debugging-machine-learning-tasks/embed/#?secret=zY10xfWUOx" width="600" height="338" title="&#x201C;Debugging Machine Learning Tasks&#x201D; &#x2014; Microsoft Research" data-secret="zY10xfWUOx" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and [&hellip;]</description></oembed>
