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	<title>data Archives - Petamind</title>
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	<title>data Archives - Petamind</title>
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	<item>
		<title>Dealing with missing data</title>
		<link>https://petaminds.com/dealing-with-missing-data/</link>
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		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Fri, 11 Jun 2021 21:15:01 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[missing]]></category>
		<category><![CDATA[preprocessing]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2925</guid>

					<description><![CDATA[<p>In real-world data, there are some instances where a particular element is absent because of various reasons, such as corrupt data, failure to load the information, or incomplete extraction. Handling the missing values is one of the greatest challenges faced by analysts because making the right decision on how to handle it generates robust data models. Let [&#8230;]</p>
<p>The post <a href="https://petaminds.com/dealing-with-missing-data/">Dealing with missing data</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>Generate data on the fly &#8211; Keras data generator</title>
		<link>https://petaminds.com/generate-data-on-the-fly-keras-data-generator/</link>
					<comments>https://petaminds.com/generate-data-on-the-fly-keras-data-generator/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Fri, 31 Jan 2020 23:13:05 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[generator]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[sequence]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1906</guid>

					<description><![CDATA[<p>Previously, we train our model using the pre-generated dataset, for example, in the recommender system or recurrent neural network. In this article, we will demonstrate using a generator to produce data on the fly for training a model. Keras Data Generator with Sequence There are a couple of ways to create a data generator. However, [&#8230;]</p>
<p>The post <a href="https://petaminds.com/generate-data-on-the-fly-keras-data-generator/">Generate data on the fly &#8211; Keras data generator</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>Recurrent neural network &#8211; predict monthly milk production</title>
		<link>https://petaminds.com/recurrent-neural-network-predict-monthly-milk-production/</link>
					<comments>https://petaminds.com/recurrent-neural-network-predict-monthly-milk-production/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 28 Nov 2019 20:50:24 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[LSTM]]></category>
		<category><![CDATA[milk]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[production]]></category>
		<category><![CDATA[recurrent]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1904</guid>

					<description><![CDATA[<p>In part 1, we introduced a simple RNN for time-series data. To continue, this article applies a deep version of RNN on a real dataset to predict monthly milk production. The data Monthly milk production: pounds per cow. Jan 1962 &#8211; Dec 1975. You can download the data using this link. Download: CSV file The [&#8230;]</p>
<p>The post <a href="https://petaminds.com/recurrent-neural-network-predict-monthly-milk-production/">Recurrent neural network &#8211; predict monthly milk production</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>A really Cool data visualization: 3d globe in 2d space</title>
		<link>https://petaminds.com/a-really-cool-data-visualization-3d-globe-in-2d-space/</link>
					<comments>https://petaminds.com/a-really-cool-data-visualization-3d-globe-in-2d-space/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 11 Nov 2019 00:43:30 +0000</pubDate>
				<category><![CDATA[Android]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Game Dev]]></category>
		<category><![CDATA[iOS]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[animation]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[Kotlin]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[visualization]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1752</guid>

					<description><![CDATA[<p>While generating data in 3d space for manifold learning, I went across a problem of distributing points evenly on a sphere. It is a non-trivial problem but found a good enough solution for such placement. Interestingly, it ends up with a really cool animation effect when I decided to implement it on a mobile app. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/a-really-cool-data-visualization-3d-globe-in-2d-space/">A really Cool data visualization: 3d globe in 2d space</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>TF2.0 Warm-up exercises (forked from @chipHuyen Repo)</title>
		<link>https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/</link>
					<comments>https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Fri, 08 Nov 2019 01:54:24 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[exercise]]></category>
		<category><![CDATA[matrix]]></category>
		<category><![CDATA[tensorflow]]></category>
		<category><![CDATA[tf2]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1681</guid>

					<description><![CDATA[<p>Heard of Ms @huyen chip for her notable yet controversial travelling books back in the day. I enjoy reading but I am not really into travel memoirs. Nevertheless, she did surprise everyone by her achievements by getting in Stanford, teaching TensorFlow, and then became a computer/data scientist. Her story is definitely very inspiring. For ones who [&#8230;]</p>
<p>The post <a href="https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/">TF2.0 Warm-up exercises (forked from @chipHuyen Repo)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>Data Visualization &#8211; Charts with Matplotlib</title>
		<link>https://petaminds.com/data-visualization-matplotlib-python-chart/</link>
					<comments>https://petaminds.com/data-visualization-matplotlib-python-chart/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 07 Nov 2019 03:41:09 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[visualization]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[line]]></category>
		<category><![CDATA[matplotlib]]></category>
		<category><![CDATA[pair]]></category>
		<category><![CDATA[plot]]></category>
		<category><![CDATA[scatter]]></category>
		<category><![CDATA[stack]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1738</guid>

					<description><![CDATA[<p>A common use for notebooks is data visualization using charts. It is easy with several charting tools available as Python imports. This article covers some common charts using matplotlib. Matplotlib Matplotlib&#160;is the most common charting package, see its&#160;documentation&#160;for details, and its&#160;examples&#160;for inspiration. Charting</p>
<p>The post <a href="https://petaminds.com/data-visualization-matplotlib-python-chart/">Data Visualization &#8211; Charts with Matplotlib</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>New TensorFlow 2.0 vs 1.X &#8211; Quick note</title>
		<link>https://petaminds.com/new-tensorflow-2-0-vs-1-x-quick-note/</link>
					<comments>https://petaminds.com/new-tensorflow-2-0-vs-1-x-quick-note/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 10 Oct 2019 08:25:53 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[pytorch]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1048</guid>

					<description><![CDATA[<p>TensorFlow 2.0 is out! Get hands-on practice at TF World, Oct 28-31. TensorFlow Ads Since the TF2.0 API reference lists have already been made publicly available, TF2.0 is still in RC.2 version. It is expected that the final release will be made available in the next few days (or weeks). What&#8217;s new in TF2.0: The [&#8230;]</p>
<p>The post <a href="https://petaminds.com/new-tensorflow-2-0-vs-1-x-quick-note/">New TensorFlow 2.0 vs 1.X &#8211; Quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>AI in agriculture: fruit grading  (Part 1)</title>
		<link>https://petaminds.com/use-ai-technology-in-agriculture-for-fruit-grading-part-1/</link>
					<comments>https://petaminds.com/use-ai-technology-in-agriculture-for-fruit-grading-part-1/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 26 Sep 2019 03:37:27 +0000</pubDate>
				<category><![CDATA[back-end]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[fruit]]></category>
		<category><![CDATA[fruit grading]]></category>
		<category><![CDATA[Machine learning]]></category>
		<category><![CDATA[neural network]]></category>
		<guid isPermaLink="false">http://petaminds.com/?p=645</guid>

					<description><![CDATA[<p>During a meet up last month, a friend told me about the current project on a farm in New Zealand. They want to build a system to grade their fruits and AI is the technology they are looking for. It inspired me to write about how machine learning can help in solving such a problem. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/use-ai-technology-in-agriculture-for-fruit-grading-part-1/">AI in agriculture: fruit grading  (Part 1)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>ETL (Extract, Transform, and Load) Process Quick note</title>
		<link>https://petaminds.com/etl-extract-transform-and-load-process-quick-note/</link>
					<comments>https://petaminds.com/etl-extract-transform-and-load-process-quick-note/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 18 Feb 2019 07:55:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[ETA]]></category>
		<category><![CDATA[extract]]></category>
		<category><![CDATA[load]]></category>
		<category><![CDATA[process]]></category>
		<category><![CDATA[transform]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1825</guid>

					<description><![CDATA[<p>You could have been playing with this concept so many times without knowing the name of ETL. Let have a quick concept review using MovieLens automation as examples. Concept According to Wikipedia: Extract, Transform and Load (ETL) refers to a process in database usage and especially in data warehousing that: Extracts data from homogeneous or [&#8230;]</p>
<p>The post <a href="https://petaminds.com/etl-extract-transform-and-load-process-quick-note/">ETL (Extract, Transform, and Load) Process Quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>Machine learning quick note</title>
		<link>https://petaminds.com/machine-learning-quick-note/</link>
					<comments>https://petaminds.com/machine-learning-quick-note/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 01 Oct 2018 09:43:19 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[algorithm]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[Machine learning]]></category>
		<category><![CDATA[python]]></category>
		<guid isPermaLink="false">http://petaminds.com/?p=355</guid>

					<description><![CDATA[<p>Machine learning is a terminology to describe the uses statistical techniques to give computer systems the ability to &#8220;learn&#8221; (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. You can think of machine learning as the brains&#160;behind AI technologies, and AI technologies do the actions.&#160;More technically, machine learning is the [&#8230;]</p>
<p>The post <a href="https://petaminds.com/machine-learning-quick-note/">Machine learning quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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