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	<title>Gigaom Search &#187; Technologies and Products &#187; machine learning</title>
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		<title>Remember when machine learning was hard? That&#8217;s about to change</title>
		<link>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/</link>
		<comments>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/#comments</comments>
		<pubDate>Sat, 21 Feb 2015 17:56:40 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=916311</guid>
		<description><![CDATA[A few years ago, there was a shift in the world of machine learning. Companies, such as Skytree and Context Relevant, began popping up, promising to make it easier for companies&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
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		<title>The 4 things (at least) you&#8217;ll learn about at Structure Data</title>
		<link>http://gigaom.com/2015/02/19/the-4-things-at-least-youll-learn-about-at-structure-data/</link>
		<comments>http://gigaom.com/2015/02/19/the-4-things-at-least-youll-learn-about-at-structure-data/#comments</comments>
		<pubDate>Thu, 19 Feb 2015 17:34:45 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Structure Data 2015]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=915784</guid>
		<description><![CDATA[Gigaom's Structure Data conference is less than a month away, kicking off March 18 in New York. There are a lot of reasons to attend -- great location, great networking, free drinks --&#8230;]]></description>
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		<slash:comments>1</slash:comments>
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		<title>Why deep learning is at least inspired by biology, if not the brain</title>
		<link>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/</link>
		<comments>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/#comments</comments>
		<pubDate>Sat, 14 Feb 2015 18:58:13 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Computational neuroscience]]></category>
		<category><![CDATA[Enlitic]]></category>
		<category><![CDATA[health care]]></category>
		<category><![CDATA[healthcare]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[neuroscience]]></category>
		<category><![CDATA[object recognition]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=915084</guid>
		<description><![CDATA[As deep learning continues gathering steam among researchers, entrepreneurs and the press, there's a loud-and-getting-louder debate about whether its algorithms actually operate like the human brain does. The comparison might not make much&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
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		<title>Microsoft says its new computer vision system can outperform humans</title>
		<link>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/</link>
		<comments>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/#comments</comments>
		<pubDate>Fri, 13 Feb 2015 19:17:35 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[image recognition]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Microsoft Research]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=914914</guid>
		<description><![CDATA[Microsoft researchers claim in a recently published paper that they have developed the first computer system capable of outperforming humans on a popular benchmark. While it's estimated that humans can classify images in&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
		</item>
		<item>
		<title>Here&#8217;s more evidence that sports is a goldmine for machine learning</title>
		<link>http://gigaom.com/2015/02/12/heres-more-evidence-that-sports-is-a-goldmine-for-machine-learning/</link>
		<comments>http://gigaom.com/2015/02/12/heres-more-evidence-that-sports-is-a-goldmine-for-machine-learning/#comments</comments>
		<pubDate>Thu, 12 Feb 2015 19:09:41 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Automated Insights]]></category>
		<category><![CDATA[language understanding]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[sports data]]></category>
		<category><![CDATA[statistics]]></category>
		<category><![CDATA[stats]]></category>
		<category><![CDATA[text analysis]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=914504</guid>
		<description><![CDATA[If you really like sports and you're really skilled at data analysis or machine learning, you might want to make that your profession. On Thursday, private equity firm Vista announced it&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/12/heres-more-evidence-that-sports-is-a-goldmine-for-machine-learning/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Machine Learning for business users and enterprise developers</title>
		<link>http://research.gigaom.com/webinar/machine-learning-for-business-users-and-enterprise-developers/</link>
		<comments>http://research.gigaom.com/webinar/machine-learning-for-business-users-and-enterprise-developers/#comments</comments>
		<pubDate>Thu, 12 Feb 2015 18:00:00 +0000</pubDate>
		<dc:creator><![CDATA[Andrew Brust]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
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		<guid isPermaLink="false">http://research.gigaom.com/?post_type=go_webinar&#038;p=244895</guid>
		<description><![CDATA[Machine Learning has been around for well over a decade and, with the rise of “Big Data” analytics, has become especially prominent in the last few years.  Many companies are now&#8230;]]></description>
		<wfw:commentRss>http://research.gigaom.com/webinar/machine-learning-for-business-users-and-enterprise-developers/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>No, you don&#8217;t need a ton of data to do deep learning</title>
		<link>http://gigaom.com/2015/02/12/no-you-dont-need-a-ton-of-data-to-do-deep-learning/</link>
		<comments>http://gigaom.com/2015/02/12/no-you-dont-need-a-ton-of-data-to-do-deep-learning/#comments</comments>
		<pubDate>Thu, 12 Feb 2015 08:01:33 +0000</pubDate>
		<dc:creator><![CDATA[Barb Darrow]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Enlitic]]></category>
		<category><![CDATA[Jeremy Howard]]></category>
		<category><![CDATA[Structure Show]]></category>
		<category><![CDATA[The Structure Show]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:AMZN]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:IBM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=914355</guid>
		<description><![CDATA[[soundcloud url="https://api.soundcloud.com/tracks/190680894" params="secret_token=s-lutIw&#38;color=ff5500&#38;auto_play=false&#38;hide_related=false&#38;show_comments=true&#38;show_user=true&#38;show_reposts=false" width="100%" height="166" iframe="true" /] There are a couple of seemingly contradictory memes rolling around the deep learning field. One is that you need a truly epic amount of data to&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/12/no-you-dont-need-a-ton-of-data-to-do-deep-learning/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<title>DARPA shows off its tech for indexing the deep web</title>
		<link>http://gigaom.com/2015/02/09/darpa-shows-off-its-tech-for-indexing-the-deep-web/</link>
		<comments>http://gigaom.com/2015/02/09/darpa-shows-off-its-tech-for-indexing-the-deep-web/#comments</comments>
		<pubDate>Mon, 09 Feb 2015 20:54:38 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Human trafficking]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[national security]]></category>
		<category><![CDATA[web security]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=913597</guid>
		<description><![CDATA[On Sunday night, 60 Minutes aired a segment about the Defense Advanced Research Projects Agency, or DARPA, and its attempts to secure the internet from hackers, human traffickers and other criminals. One of&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/09/darpa-shows-off-its-tech-for-indexing-the-deep-web/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>TeraDeep wants to bring deep learning to your dumb devices</title>
		<link>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/</link>
		<comments>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/#comments</comments>
		<pubDate>Mon, 02 Feb 2015 21:00:54 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[FPGAs]]></category>
		<category><![CDATA[hardware]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[microprocessors]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
		<category><![CDATA[Structure Data awards]]></category>
		<category><![CDATA[TeraDeep]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=911680</guid>
		<description><![CDATA[Open the closet of any gadget geek or computer nerd, and you're likely to find a lot of skeletons. Stacked deep in a cardboard box or Tupperware tub, there they are:&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>New to deep learning? Here are 4 easy lessons from Google</title>
		<link>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/</link>
		<comments>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/#comments</comments>
		<pubDate>Fri, 30 Jan 2015 01:26:11 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[object recognition]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=911067</guid>
		<description><![CDATA[Google employs some of the world's smartest researchers in deep learning and artificial intelligence, so it's not a bad idea to listen to what they have to say about the space. One&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
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