<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Fri, 10 Apr 2026 21:53:10 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/@jobsua2018/rss" rel="self" type="application/rss+xml"/><item><title><![CDATA[Neural Network to play a snake game – Towards Data Science part 2]]></title><description><![CDATA[Input data The neural network need some data to learn on. Input data is very important part of machine learning. If you have a huge amount of data, you can achieve great results even if an architecture]]></description><link>http://direct.ecency.com/neural-network/@jobsua2018/neural-network-to-play-a-snake-game-towards-data-science-part-2</link><guid isPermaLink="true">http://direct.ecency.com/neural-network/@jobsua2018/neural-network-to-play-a-snake-game-towards-data-science-part-2</guid><category><![CDATA[neural-network]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Mon, 05 Mar 2018 17:18:57 GMT</pubDate><enclosure url="https://images.ecency.com/p/EfcLDDAkyqgqyc52TdHnx88BjGJXpnzxmEnB8yNLCo37xWGfMtGtRsLqSgqGd9siKT4QZF1srNrSzv8faEic1NgNrWuvW?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Neural Network to play a snake game – Towards Data Science part 1]]></title><description><![CDATA[something about machine learning, neural networks, and TensorFlow but there is no problem otherwise. And finally, obviously, there are better approaches to write a logic for a snake game but let’s pretend]]></description><link>http://direct.ecency.com/neural-network/@jobsua2018/neural-network-to-play-a-snake-game-towards-data-science-part-1</link><guid isPermaLink="true">http://direct.ecency.com/neural-network/@jobsua2018/neural-network-to-play-a-snake-game-towards-data-science-part-1</guid><category><![CDATA[neural-network]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Mon, 05 Mar 2018 10:10:57 GMT</pubDate><enclosure url="https://images.ecency.com/p/EfcLDDAkyqgqyc52TdHnx88BjGJXpnzxmEnB8yNLCo37xWGfMtGtRsLqSekKWWd5kVum6kj2Mdn2jGFvz8jYWTGFiuuT3?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Hail technology: Deep learning may help predict when people need rides]]></title><description><![CDATA[Computers may better predict taxi and ride sharing service demand, paving the way toward smarter, safer and more sustainable cities, according to an international team of researchers. In a study, the]]></description><link>http://direct.ecency.com/neural-network/@jobsua2018/hail-technology-deep-learning-may-help-predict-when-people-need-rides</link><guid isPermaLink="true">http://direct.ecency.com/neural-network/@jobsua2018/hail-technology-deep-learning-may-help-predict-when-people-need-rides</guid><category><![CDATA[neural-network]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Sun, 04 Mar 2018 12:38:48 GMT</pubDate></item><item><title><![CDATA[AI (artificial intelligence)]]></title><description><![CDATA[AI (pronounced AYE-EYE) or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information]]></description><link>http://direct.ecency.com/programs/@jobsua2018/ai-artificial-intelligence</link><guid isPermaLink="true">http://direct.ecency.com/programs/@jobsua2018/ai-artificial-intelligence</guid><category><![CDATA[programs]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Mon, 19 Feb 2018 00:30:09 GMT</pubDate></item><item><title><![CDATA[CRYPTO CREATOR? Bitcoin creator whose identity is unknown but could be one of the world’s richest people]]></title><description><![CDATA[CRYPTO CREATOR Who is Satoshi Nakamoto? Bitcoin creator whose identity is unknown but could be one of the world’s richest #people #Bitcoin has taken the financial #world by storm, but who is the creator]]></description><link>http://direct.ecency.com/cryptocurrency/@jobsua2018/crypto-creator-bitcoin-creator-whose-identity-is-unknown-but-could-be-one-of-the-world-s-richest-people</link><guid isPermaLink="true">http://direct.ecency.com/cryptocurrency/@jobsua2018/crypto-creator-bitcoin-creator-whose-identity-is-unknown-but-could-be-one-of-the-world-s-richest-people</guid><category><![CDATA[cryptocurrency]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Fri, 09 Feb 2018 12:16:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/7NqGr5y7HSV2oXLpWymjPmr94R37KfrFnVo39wReNhSDBUwtctGxzwAv579dz3XHaxAXir9E5P6PqjgEUzC9eAXqcgBdPEBtfR7p3Ern3vo7YzUiQGUgcUEfq5xfea7atrJBZohqKASEHa9tskNtaxF5peWat6ZXwLEFqc8pnSKCWXStnWBrYxh4WJsg6FctKBrpMLBmK4W1y7BmQoH1?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Coding Neural Network Back-Propagation Using C# part 3]]></title><description><![CDATA[Next, the input-to-hidden weight gradients and the hidden bias gradients are calculated: for (int i = 0; i < numInput; ++i) for (int j = 0; j < numHidden; ++j) ihGrads\[i\]\[j\] = hSignals\[j\] *]]></description><link>http://direct.ecency.com/neural-network/@jobsua2018/coding-neural-network-back-propagation-using-c-part-3</link><guid isPermaLink="true">http://direct.ecency.com/neural-network/@jobsua2018/coding-neural-network-back-propagation-using-c-part-3</guid><category><![CDATA[neural-network]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Fri, 09 Feb 2018 06:38:27 GMT</pubDate></item><item><title><![CDATA[Parse HTML in .NET and survive: an analysis and comparison of libraries]]></title><description><![CDATA[In the course of working on a home project, faced with the need of parsing HTML. Search on Google gave comment Athari and his micro-review of the current parsers in HTML .NET for which he thanks.]]></description><link>http://direct.ecency.com/programming/@jobsua2018/parse-html-in-net-and-survive-an-analysis-and-comparison-of-libraries</link><guid isPermaLink="true">http://direct.ecency.com/programming/@jobsua2018/parse-html-in-net-and-survive-an-analysis-and-comparison-of-libraries</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Thu, 08 Feb 2018 13:56:57 GMT</pubDate><enclosure url="https://images.ecency.com/p/2923mN3pnd7PrxqAS8My84z8QnDpEsSBnKFsBYXvnY2mbNDBGZd16vDUAaGddPN7CDA76KZgr428Mp7KA5YMH64MMZX4AKLVMWM3tvR49Z1xNi?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Ridiculous]]></title><description><![CDATA[Which one do you like best?]]></description><link>http://direct.ecency.com/funy/@jobsua2018/ridiculous</link><guid isPermaLink="true">http://direct.ecency.com/funy/@jobsua2018/ridiculous</guid><category><![CDATA[funy]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Thu, 08 Feb 2018 03:14:48 GMT</pubDate><enclosure url="https://images.ecency.com/p/TZjG7hXReeVthqpTJZmwvzsLcvMeG4TU4Gjdfy25no1TnCTR7BSTuD7AUy881foUcidViZZChhM3K3HaQDs4fs4FMVH5nfsHRwJLcx7yCDUR8ZjpuKYgfGJyUGcx7Lff5qpTmwa6RoktSz?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Coding Neural Network Back-Propagation Using C# part 2]]></title><description><![CDATA[Vote. and the second part will be released. the source in the last part. var example = true Training using back-propagation is accomplished with these statements: Console.WriteLine("Starting]]></description><link>http://direct.ecency.com/deep-learning/@jobsua2018/coding-neural-network-back-propagation-using-c-part-2</link><guid isPermaLink="true">http://direct.ecency.com/deep-learning/@jobsua2018/coding-neural-network-back-propagation-using-c-part-2</guid><category><![CDATA[deep-learning]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Wed, 07 Feb 2018 19:17:39 GMT</pubDate><enclosure url="https://images.ecency.com/p/54TLbcUcnRm4iYtFdzVNy1kt3F2tvRShXkTnWxjMqKXcNXwEjaBy8V8TsEbRc4cwWc4Qh4DJK61uQVZJWz5XK9QCNTVLc3RA3AWaABAi5tPjDxd5g9DvtuZeZ73BaD87tRdntBSEJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Coding Neural Network Back-Propagation Using C# part 1]]></title><description><![CDATA[Vote. and the second part will be released. the source in the last part. var example = true Back-propagation is the most common algorithm used to train neural networks. There are many ways that]]></description><link>http://direct.ecency.com/programing/@jobsua2018/coding-neural-network-back-propagation-using-c-part-1</link><guid isPermaLink="true">http://direct.ecency.com/programing/@jobsua2018/coding-neural-network-back-propagation-using-c-part-1</guid><category><![CDATA[programing]]></category><dc:creator><![CDATA[jobsua2018]]></dc:creator><pubDate>Wed, 07 Feb 2018 12:37:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/vM1pGHgNcyCiaxZmdb555hkjecmqomntNdRp7jgAB1DojyVC97LEA27XuyhzhC4YswW5FsPWhrSxCxG7PGXz4GpEuraqZNYd4AqSKwionofBgkdYLugm32YboE9AbvfU3J2hTbU?format=match&amp;mode=fit" length="0" type="false"/></item></channel></rss>