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	<title>Remember Lenny</title>
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		<title>How Public Art works</title>
		<link>/2019/03/07/how-public-art-works/</link>
					<comments>/2019/03/07/how-public-art-works/#comments</comments>
		
		<dc:creator><![CDATA[rememberlenny]]></dc:creator>
		<pubDate>Thu, 07 Mar 2019 15:40:38 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Rails]]></category>
		<category><![CDATA[React]]></category>
		<category><![CDATA[React Native]]></category>
		<category><![CDATA[Street Art]]></category>
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					<description><![CDATA[Details on my React-Native iOS application backed by a Ruby on Rails backend and some Python Jupyter notebook scripts Public Art is an iOS application that helps you discover new nearby street art. I’ve been working on this project on my own, but it has a lot of technical moving parts. I will explain how all of [&#8230;]
<p><a href="/2019/03/07/how-public-art-works/" rel="nofollow">Source</a></p>]]></description>
		
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			<slash:comments>5</slash:comments>
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">741</post-id>	</item>
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		<title>Weekly update February 19, 2019</title>
		<link>/2019/02/20/weekly-update-february-19-2019/</link>
					<comments>/2019/02/20/weekly-update-february-19-2019/#comments</comments>
		
		<dc:creator><![CDATA[rememberlenny]]></dc:creator>
		<pubDate>Wed, 20 Feb 2019 04:49:16 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Public Art]]></category>
		<category><![CDATA[Street Art]]></category>
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					<description><![CDATA[Bi-weekly update for February 19th, 2019 Hey! I promised a bi-weekly update, so heres #2! I did a lot of new development this past two weeks. To kick it off, I started experimenting with social media ads, selling physical products, built and released 21 versions of an app, majorly upgraded my backend application, and finally got [&#8230;]
<p><a href="/2019/02/20/weekly-update-february-19-2019/" rel="nofollow">Source</a></p>]]></description>
		
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			<slash:comments>3</slash:comments>
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">743</post-id>	</item>
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		<title>On Building an Instagram Street Art Dataset and Detection Model</title>
		<link>/2019/02/13/on-building-an-instagram-street-art-dataset-and-detection-model/</link>
					<comments>/2019/02/13/on-building-an-instagram-street-art-dataset-and-detection-model/#comments</comments>
		
		<dc:creator><![CDATA[rememberlenny]]></dc:creator>
		<pubDate>Wed, 13 Feb 2019 17:30:11 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[Digital Humanities]]></category>
		<category><![CDATA[Instagram Marketing]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Street Art]]></category>
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					<description><![CDATA[What if you could pump all of the Instagram photos of Banksy’s artwork into a program that could pinpoint where the next one’s likely to be? Well, we aren’t there quite yet, but there’s still some really cool stuff you can accomplish using image analysis and machine learning to better understand street art. You can [&#8230;]
<p><a href="/2019/02/13/on-building-an-instagram-street-art-dataset-and-detection-model/" rel="nofollow">Source</a></p>]]></description>
		
					<wfw:commentRss>/2019/02/13/on-building-an-instagram-street-art-dataset-and-detection-model/feed/</wfw:commentRss>
			<slash:comments>1</slash:comments>
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">745</post-id>	</item>
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		<title>Tracking street art with machine learning — updates</title>
		<link>/2018/11/08/tracking-street-art-with-machine-learning%e2%80%8a-%e2%80%8aupdates/</link>
					<comments>/2018/11/08/tracking-street-art-with-machine-learning%e2%80%8a-%e2%80%8aupdates/#comments</comments>
		
		<dc:creator><![CDATA[rememberlenny]]></dc:creator>
		<pubDate>Thu, 08 Nov 2018 14:36:46 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Art]]></category>
		<category><![CDATA[Graffiti]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Street Art]]></category>
		<category><![CDATA[Towards Data Science]]></category>
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					<description><![CDATA[Mural from Reyes, Revok and Steel from MSK (https://www.fatcap.com/live/revok-steel-and-reyes.html) Thank you for following the Public Art[⁰] project for building a genealogy around street art, using machine learning. This project is aiming to create a central place for documenting street art from around the world, and use modern image analysis techniques to build a historical reference [&#8230;]
<p><a href="/2018/11/08/tracking-street-art-with-machine-learning%e2%80%8a-%e2%80%8aupdates/" rel="nofollow">Source</a></p>]]></description>
		
					<wfw:commentRss>/2018/11/08/tracking-street-art-with-machine-learning%e2%80%8a-%e2%80%8aupdates/feed/</wfw:commentRss>
			<slash:comments>2</slash:comments>
		
		
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