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	<title>Research Archives - Petamind</title>
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	<title>Research Archives - Petamind</title>
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	<item>
		<title>Exploring Kotlin&#8217;s suspendCoroutine: A Guide with Examples</title>
		<link>https://petaminds.com/exploring-kotlins-suspendcoroutine-a-guide-with-examples/</link>
					<comments>https://petaminds.com/exploring-kotlins-suspendcoroutine-a-guide-with-examples/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 08 Aug 2023 22:51:56 +0000</pubDate>
				<category><![CDATA[Android]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[full-stack]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Kotlin]]></category>
		<category><![CDATA[suspendCoroutine]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3948</guid>

					<description><![CDATA[<p>Introduction: Kotlin, with its modern syntax and powerful features, has taken the programming world by storm. One of its unique offerings is the suspendCoroutine function, a game-changer when it comes to handling asynchronous operations. In this article, we&#8217;ll dive into the world of suspendCoroutine, demystifying its usage and providing you with real-world examples to showcase [&#8230;]</p>
<p>The post <a href="https://petaminds.com/exploring-kotlins-suspendcoroutine-a-guide-with-examples/">Exploring Kotlin&#8217;s suspendCoroutine: A Guide with Examples</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>The Top 5 Most In-Demand Programming Languages to Learn for a Bright Future</title>
		<link>https://petaminds.com/the-top-5-most-in-demand-programming-languages-to-learn-for-a-bright-future/</link>
					<comments>https://petaminds.com/the-top-5-most-in-demand-programming-languages-to-learn-for-a-bright-future/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 02 Aug 2023 02:27:06 +0000</pubDate>
				<category><![CDATA[Android]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[full-stack]]></category>
		<category><![CDATA[Research]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3931</guid>

					<description><![CDATA[<p>Introduction: In the fast-paced world of technology, staying ahead of the curve is crucial for aspiring programmers. As the demand for software developers continues to surge, knowing which programming languages are most sought-after can give you a competitive edge in the job market. In this article, we&#8217;ll explore the top five most in-demand programming languages [&#8230;]</p>
<p>The post <a href="https://petaminds.com/the-top-5-most-in-demand-programming-languages-to-learn-for-a-bright-future/">The Top 5 Most In-Demand Programming Languages to Learn for a Bright Future</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>Advanced Keras &#8211; Custom loss functions</title>
		<link>https://petaminds.com/advanced-keras-custom-loss-functions/</link>
					<comments>https://petaminds.com/advanced-keras-custom-loss-functions/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 23 Mar 2022 00:31:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[cost function]]></category>
		<category><![CDATA[custom loss]]></category>
		<category><![CDATA[K]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[keras backend]]></category>
		<category><![CDATA[loss function]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1391</guid>

					<description><![CDATA[<p>When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often pass two parameters, i.e. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/advanced-keras-custom-loss-functions/">Advanced Keras &#8211; Custom loss functions</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>Understanding Latent Dirichlet Allocation (LDA)</title>
		<link>https://petaminds.com/understanding-latent-dirichlet-allocation-lda/</link>
					<comments>https://petaminds.com/understanding-latent-dirichlet-allocation-lda/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 02 Jan 2022 03:37:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[latent dirichlet allocation]]></category>
		<category><![CDATA[lda]]></category>
		<category><![CDATA[NLP]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3625</guid>

					<description><![CDATA[<p>Imagine a large law firm takes over a smaller law firm and tries to identify the documents corresponding to different types of cases such as civil or criminal cases which the smaller firm has dealt or is currently dealing with. The presumption is that the documents are not already classified by the smaller law firm. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/understanding-latent-dirichlet-allocation-lda/">Understanding Latent Dirichlet Allocation (LDA)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>How to Write a Research Question</title>
		<link>https://petaminds.com/how-to-write-a-research-question/</link>
					<comments>https://petaminds.com/how-to-write-a-research-question/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 30 Nov 2021 22:13:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[question]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[scientific]]></category>
		<category><![CDATA[writing]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3583</guid>

					<description><![CDATA[<p>The research question, if correctly completed, will help you to set out what it is that you want to answer. This can help you make a plan for your research, but might also help you to foresee any potential challenges or problems. This will save you time, energy, and effort. Therefore, it is extremely important [&#8230;]</p>
<p>The post <a href="https://petaminds.com/how-to-write-a-research-question/">How to Write a Research Question</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>Feature Engineering FundamentalS</title>
		<link>https://petaminds.com/feature-engineering-fundamentals/</link>
					<comments>https://petaminds.com/feature-engineering-fundamentals/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 22 Sep 2020 11:20:23 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[feature]]></category>
		<category><![CDATA[one-hot]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[scaling]]></category>
		<category><![CDATA[split]]></category>
		<category><![CDATA[standardization]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2714</guid>

					<description><![CDATA[<p>The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct&#160;feature engineering. — Luca Massaron What is a feature and why we need engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data [&#8230;]</p>
<p>The post <a href="https://petaminds.com/feature-engineering-fundamentals/">Feature Engineering FundamentalS</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>Top programming languages to learn 2020-2021</title>
		<link>https://petaminds.com/top-programming-languages-to-learn-2020-2021/</link>
					<comments>https://petaminds.com/top-programming-languages-to-learn-2020-2021/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 30 Jul 2020 22:38:00 +0000</pubDate>
				<category><![CDATA[full-stack]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[2020]]></category>
		<category><![CDATA[2021]]></category>
		<category><![CDATA[2022]]></category>
		<category><![CDATA[GO]]></category>
		<category><![CDATA[JAVA]]></category>
		<category><![CDATA[Kotlin]]></category>
		<category><![CDATA[programming]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[Rust]]></category>
		<category><![CDATA[SWIFT]]></category>
		<category><![CDATA[top language]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2475</guid>

					<description><![CDATA[<p>&#8220;What is the best programming language to learn&#8221; is generally not a good question. A language should bind to a purpose, developing environment. Set it aside, popular languages often come with trending jobs. Here is the list of top 7 programming languages to consider in 2020 and 2021. 1.  Python The programming language continues to [&#8230;]</p>
<p>The post <a href="https://petaminds.com/top-programming-languages-to-learn-2020-2021/">Top programming languages to learn 2020-2021</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>Advanced python: comprehension</title>
		<link>https://petaminds.com/advanced-python-comprehension/</link>
					<comments>https://petaminds.com/advanced-python-comprehension/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 08 Jul 2020 22:38:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[comprehension]]></category>
		<category><![CDATA[dictionary]]></category>
		<category><![CDATA[lambda]]></category>
		<category><![CDATA[list]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[set]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2459</guid>

					<description><![CDATA[<p>In this article, we&#8217;ll learn&#160;about a Python language construct known as comprehensions for creating a new sequence based on existing one but its syntax is more human-readable than lambda functions. These can be applied to lists, sets, and dictionaries.&#160; REVIEW Lambda and map Lambda is an anonymous function (function without name). Let have a quick [&#8230;]</p>
<p>The post <a href="https://petaminds.com/advanced-python-comprehension/">Advanced python: comprehension</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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			</item>
		<item>
		<title>Research Design: Definition, Characteristics and Types</title>
		<link>https://petaminds.com/research-design-definition-characteristics-and-types/</link>
					<comments>https://petaminds.com/research-design-definition-characteristics-and-types/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 14 Jun 2020 00:36:20 +0000</pubDate>
				<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[design]]></category>
		<category><![CDATA[research]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2643</guid>

					<description><![CDATA[<p>A&#160;research design&#160;is the set of methods and procedures used in collecting and analyzing measures of the variables specified in the problem&#160;research. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal&#160;case study),&#160;research problem,&#160;hypotheses,&#160;independent and dependent variables,&#160;experimental design, and, if applicable, data collection methods and a statistical [&#8230;]</p>
<p>The post <a href="https://petaminds.com/research-design-definition-characteristics-and-types/">Research Design: Definition, Characteristics and Types</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>Continue training big models on less powerful devices</title>
		<link>https://petaminds.com/continue-training-big-models-on-less-powerful-devices/</link>
					<comments>https://petaminds.com/continue-training-big-models-on-less-powerful-devices/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 22 Mar 2020 00:51:57 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[check-point]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[model]]></category>
		<category><![CDATA[out of memory]]></category>
		<category><![CDATA[save]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2338</guid>

					<description><![CDATA[<p>It would not be a surprise that you may not have a powerful expensive machine to train a complicate model. You may experience the problem of not enough memory during training in some epoch. This article demonstrates a simple workaround for this. The problem Training deep learning models requires a lot of computing power. For [&#8230;]</p>
<p>The post <a href="https://petaminds.com/continue-training-big-models-on-less-powerful-devices/">Continue training big models on less powerful devices</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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