<?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>Wed, 08 Apr 2026 00:55:36 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/@kasperfred/rss" rel="self" type="application/rss+xml"/><item><title><![CDATA[How Ray-tracing and rendering works.]]></title><description><![CDATA[ I go through how ray-tracing works and the mathematics behind it.  You can read the full article on my website: kasperfred.com. No! I refuse to talk about neural networks even though they offer]]></description><link>http://direct.ecency.com/programming/@kasperfred/how-ray-tracing-and-rendering-works</link><guid isPermaLink="true">http://direct.ecency.com/programming/@kasperfred/how-ray-tracing-and-rendering-works</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Wed, 26 Jun 2019 15:51:27 GMT</pubDate><enclosure url="https://images.ecency.com/p/5bEGgqZEHBMekw7QD49pS4iyLtq1wdveXag5JUDtDLgA9HMpcuFeP9SmR9mpDDVxq7p6k5d3JXaqxp1HFNNFyPSi6KAiKU82?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Exploring The Universe With LHC]]></title><description><![CDATA[How can LHC test astrophysics phenomena? How is relativity used to calculate collisions at LHC? Are you better than a computer at finding the Higgs boson? We answer all of this, and much more in this]]></description><link>http://direct.ecency.com/physics/@kasperfred/exploring-the-universe-with-lhc</link><guid isPermaLink="true">http://direct.ecency.com/physics/@kasperfred/exploring-the-universe-with-lhc</guid><category><![CDATA[physics]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Fri, 11 Jan 2019 20:18:51 GMT</pubDate><enclosure url="https://images.ecency.com/p/DVAkPJXe6RxYQn4ZSoRgWEjgtso32Ew4GZ79Ga7BZ2J3FaoasmBg8DdGM6creELDSjXbQbGhe2LJs8?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[How to intuitively understand neural networks]]></title><description><![CDATA[A framework for thinking about neural networks, and supervised machine learning. This post originally appeared on kasperfred.com I was recently asked the question How can I better understand neural networks?]]></description><link>http://direct.ecency.com/machine-learning/@kasperfred/how-to-intuitively-understand-neural-networks</link><guid isPermaLink="true">http://direct.ecency.com/machine-learning/@kasperfred/how-to-intuitively-understand-neural-networks</guid><category><![CDATA[machine-learning]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Fri, 11 Jan 2019 20:01:48 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc16Z5R4ESaBhVFcq8AyXq5DdEgJu4FEsUNnF8AriEpR4eDvciPznJUiVKegsegAdMx3wUcCGA7qNvWixpBHSHxh5VBCUjkjiBbJ8SDdw1KdciZN7Ne?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Is it a Higgs, Computer?]]></title><description><![CDATA[Using neural networks to detect elementary particles. This article originally appeared on kasperfred.com Whoa, what a ride it has been. From all the way back in December when I wrote the "Post Mortem]]></description><link>http://direct.ecency.com/ai/@kasperfred/is-it-a-higgs-computer</link><guid isPermaLink="true">http://direct.ecency.com/ai/@kasperfred/is-it-a-higgs-computer</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Sat, 12 May 2018 11:53:12 GMT</pubDate><enclosure url="https://images.ecency.com/p/3HaJVw3AYyXBKxCfKickFKSmk7VnwducZjA3sg9WqtbeDRv79nNp589PzUPNdLuqTdgW1pPF7GYrjbfm8k5GKMWodW9RgTsynqVQFuG?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Retiring Steemit Tag Search]]></title><description><![CDATA[As some of you know, Steemit Tag Search was a project attempting to make it easier for users to find the posts they love from their favorite authors. However, despite having been functionally running since]]></description><link>http://direct.ecency.com/steem-dev/@kasperfred/retiring-steemit-tag-search</link><guid isPermaLink="true">http://direct.ecency.com/steem-dev/@kasperfred/retiring-steemit-tag-search</guid><category><![CDATA[steem-dev]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Wed, 09 May 2018 16:12:06 GMT</pubDate><enclosure url="https://images.ecency.com/p/EEEoA8oLaAxvDZG9qYrsRrhbbUfYehZ6Y1odDgmdtBBGG5MbPicySNxdHVSiQkrFXZTDXQNotLpyJX5KzZpgjH5fuXUFJmx1qoa7JQCm8PgBCLYnpcwttoe6TAN9pBbiYYeMz8uhngVgjFTiKSu4h?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[What Is A Neural Network?]]></title><description><![CDATA[Overview and introduction to feed forward neural networks. Forward propagation is discussed in detail, and we see how we might train a network. Some equations may not render properly on Steemit. For the]]></description><link>http://direct.ecency.com/programming/@kasperfred/what-is-a-neural-network</link><guid isPermaLink="true">http://direct.ecency.com/programming/@kasperfred/what-is-a-neural-network</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Tue, 27 Mar 2018 11:01:48 GMT</pubDate><enclosure url="https://images.ecency.com/p/23KQwnti57styREzGDde3R6HDcPrzTbqSs6JKCV289m8wBEKyJyoF3xEz1SmrAVTiNZqrZA53MRetPEzWAuevASwFN8wNgW?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[How Does Neural Network Backpropagation Work?]]></title><description><![CDATA[Before we can understand the backpropagation procedure, let’s first make sure that we understand how neural networks work. A neural network is essentially a bunch of operators, or neurons, that receive]]></description><link>http://direct.ecency.com/ai/@kasperfred/how-does-neural-network-backpropagation-work</link><guid isPermaLink="true">http://direct.ecency.com/ai/@kasperfred/how-does-neural-network-backpropagation-work</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Wed, 03 Jan 2018 17:38:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/2bP4pJr4wVimqCWjYimXJe2cnCgnCucLvAFU9geay6E?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Looking at German Traffic Signs]]></title><description><![CDATA[A practical application of convolutional neural networks. This article originally appeared on kasperfred.com You know, I don't think we as a species do enough looking at German traffic signs. I mean, sure,]]></description><link>http://direct.ecency.com/programming/@kasperfred/looking-at-german-traffic-signs</link><guid isPermaLink="true">http://direct.ecency.com/programming/@kasperfred/looking-at-german-traffic-signs</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Fri, 29 Dec 2017 16:09:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/BgxWBRxjvNhohZySUZxfZKP5ckg12ngsC5QFmvcZVieDPkqjTBZGxKU4DpBswsscEEqz6FzSdp3a444oDtxMfL2Eski85SrVJH5W4fV3cqYZGL3sobkApqkYBSSs5dCoZZG8zjoCFtuoLidQsUyKtqApZFiKV2oqAecF8TpdDLXME8W?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Accidental postilypost]]></title><description><![CDATA[Accidental postilypost No, nothing to see here. Continue.]]></description><link>http://direct.ecency.com/ai/@kasperfred/2fect6-post-mortem-analysis-of-my-final-year-project</link><guid isPermaLink="true">http://direct.ecency.com/ai/@kasperfred/2fect6-post-mortem-analysis-of-my-final-year-project</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Fri, 29 Dec 2017 16:01:33 GMT</pubDate></item><item><title><![CDATA[Q&A Machine learning]]></title><description><![CDATA[I've recently done a q&a session on machine learning. Here's an excerpt of what I answered. You can find the full session here. What is a formal definition of machine learning?  A system whose]]></description><link>http://direct.ecency.com/programming/@kasperfred/q-and-a-on-machine-learning</link><guid isPermaLink="true">http://direct.ecency.com/programming/@kasperfred/q-and-a-on-machine-learning</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Wed, 27 Dec 2017 13:23:45 GMT</pubDate><enclosure url="https://images.ecency.com/p/2bP4pJr4wVimqCWjYimXJe2cnCgnL4cRsr2eVdm7oYz?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Math-type Markup Support on Steem]]></title><description><![CDATA[Maybe I'm not aware of it, but I can't seem a way to figure out how to typeset math equations with the Steemit editor apart from inserting images which is not ideal for many types of posts.I was wondering]]></description><link>http://direct.ecency.com/steemit/@kasperfred/math-type-markup-support-on-steem</link><guid isPermaLink="true">http://direct.ecency.com/steemit/@kasperfred/math-type-markup-support-on-steem</guid><category><![CDATA[steemit]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Tue, 26 Dec 2017 16:30:18 GMT</pubDate><enclosure url="https://images.ecency.com/p/eAyTuUW97k85qe49eCpr1AhmMeBHv5X4kh4vErFksfmT3aCeyuxpPiTuKRxBKVwuGJxVLRHah9G?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Post Mortem Analysis of my Final Year Project]]></title><description><![CDATA[A post mortem analysis of a Data Science approach for determining the existence and decay patterns of the Higgs boson. This post originally appeared on kasperfred.com  In 2013, the CERN LHC Atlas]]></description><link>http://direct.ecency.com/programming/@kasperfred/post-mortem-analysis-of-my-final-year-project</link><guid isPermaLink="true">http://direct.ecency.com/programming/@kasperfred/post-mortem-analysis-of-my-final-year-project</guid><category><![CDATA[programming]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Tue, 26 Dec 2017 11:23:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/7258xSVeJbKmch126MMVub9e4vozqf5TRpPNeTwQQvdx6KMNiYfWK5haUBre5KbmenWNeu2dudXmQq3YkLL8eczTpp8wqyhNmAaedF1o4QWnwxPWxVEsQhLbHtRtcMh3kDq8eDmgV7FV8?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Putting it all together - Introduction to Tensorflow Part 5]]></title><description><![CDATA[This is the last part of a multi part series. If you haven't already, you should read the previous parts first. Part 1 where we discussed the design philosophy of Tensorflow. Part 2 where we discussed]]></description><link>http://direct.ecency.com/tensorflow/@kasperfred/putting-it-all-together-introduction-to-tensorflow-part-5</link><guid isPermaLink="true">http://direct.ecency.com/tensorflow/@kasperfred/putting-it-all-together-introduction-to-tensorflow-part-5</guid><category><![CDATA[tensorflow]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Fri, 13 Oct 2017 09:31:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehLUVBArqRFKSDw5CZJFbTPLMX2eczHPe7Hnk7YRviJ18LPbNDj5aGZoq1JhuRnw473i5GsXqqUBP798RcugXToxiJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Visualization - Introduction to Tensorflow Part 4]]></title><description><![CDATA[This is part four of a multi part series. If you haven't already, you should read the previous parts first. Part 1 where we discussed the design philosophy of Tensorflow. Part 2 where we discussed how]]></description><link>http://direct.ecency.com/tensorflow/@kasperfred/visualization-introduction-to-tensorflow-part-4</link><guid isPermaLink="true">http://direct.ecency.com/tensorflow/@kasperfred/visualization-introduction-to-tensorflow-part-4</guid><category><![CDATA[tensorflow]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Thu, 12 Oct 2017 06:21:33 GMT</pubDate><enclosure url="https://images.ecency.com/p/46aP2QbqUqBrPYQrGf17zyKur7RF5oP4e5n9hoVgGXugJJdAfzwnLKfK1dXbPLaCp3aMDwwexj8kmTKPvMmTR4QoV4jQ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Computations at a scale - Introduction to Tensorflow Part 3]]></title><description><![CDATA[This is part three of a multi part series. If you haven't already, you should read the previous parts first. Part 1 where we discussed the design philosophy of Tensorflow. Part 2 where we discussed how]]></description><link>http://direct.ecency.com/tensorflow/@kasperfred/computations-at-a-scale-introduction-to-tensorflow-part-3</link><guid isPermaLink="true">http://direct.ecency.com/tensorflow/@kasperfred/computations-at-a-scale-introduction-to-tensorflow-part-3</guid><category><![CDATA[tensorflow]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Wed, 11 Oct 2017 07:16:09 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehLUVBArqRFKSDw5CZJFbTPLMX2eczHPe7Hnk7YRviJ18LPbNDj5aGZoq1JhuRnw473i5GsXqqUBP798RcugXToxiJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Computations - Introduction to Tensorflow Part 2]]></title><description><![CDATA[Introduction to Tensorflow as a Computational Framework Part 2 This is part two of a multi part series. If you haven't already, you should part one first where we discussed the design philosophy of Tensorflow.]]></description><link>http://direct.ecency.com/tensorflow/@kasperfred/computations-introduction-to-tensorflow-part-2</link><guid isPermaLink="true">http://direct.ecency.com/tensorflow/@kasperfred/computations-introduction-to-tensorflow-part-2</guid><category><![CDATA[tensorflow]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Tue, 10 Oct 2017 09:30:18 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehLUVBArqRFKSDw5CZJFbTPLMX2eczHPe7Hnk7YRviJ18LPbNDj5aGZoq1JhuRnw473i5GsXqqUBP798RcugXToxiJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Design Philosophy of Tensorflow - Introduction to Tensorflow Part 1]]></title><description><![CDATA[Introduction to Tensorflow as a Computational Framework Part 1 Tensorflow is likely the most popular, and fastest growing machine learning framework that exists. With over 70000 stars on Github, and backing]]></description><link>http://direct.ecency.com/tensorflow/@kasperfred/design-philosophy-of-tensorflow-introduction-to-tensorflow-part-1</link><guid isPermaLink="true">http://direct.ecency.com/tensorflow/@kasperfred/design-philosophy-of-tensorflow-introduction-to-tensorflow-part-1</guid><category><![CDATA[tensorflow]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Mon, 09 Oct 2017 07:16:27 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehLUVBArqRFKSDw5CZJFbTPLMX2eczHPe7Hnk7YRviJ18LPbNDj5aGZoq1JhuRnw473i5GsXqqUBP798RcugXToxiJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Open-Sourcing Steem Tag Search]]></title><description><![CDATA[I'm pleased to announce that Steem Tag Search is now officially open source. Yes that's right. You can now inspect the source code of Steem Tag Search (and other apps to come) on Github. The live URL is]]></description><link>http://direct.ecency.com/steem/@kasperfred/open-sourcing-steem-tag-search</link><guid isPermaLink="true">http://direct.ecency.com/steem/@kasperfred/open-sourcing-steem-tag-search</guid><category><![CDATA[steem]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Tue, 19 Sep 2017 19:26:24 GMT</pubDate><enclosure url="https://images.ecency.com/p/8DAuGnTQCLpunQuGfHnXTmxWbRQScCVGspXNWFwLnf5PRkJz5mtMcmG4Zzib6cmQEUd6kYL5Fo4rijWpUCFngsmNyjZCFrrPjUk8upLTLz6jY2nWWLqi9sg2fteEDTAQ7M13hkWn846bamK3XqnXaPXvbf535JJFiKThRTP3dG2?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[[Quora] What is the difference between evolutionary algorithms and deep learning?]]></title><description><![CDATA[You can’t really compare those as while a deep neural network is a data structure of sorts, an evolutionary algorithm is a way of learning. This means you could have a model that is a deep learning]]></description><link>http://direct.ecency.com/ai/@kasperfred/quora-what-is-the-difference-between-evolutionary-algorithms-and-deep-learning</link><guid isPermaLink="true">http://direct.ecency.com/ai/@kasperfred/quora-what-is-the-difference-between-evolutionary-algorithms-and-deep-learning</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Sun, 10 Sep 2017 07:28:54 GMT</pubDate><enclosure url="https://images.ecency.com/p/3W72119s5BjWPGGUiZ9pqnZoj8JHYxCCp9dtn2QVgNJLfJtcAv4DQ6ges4txLDb4bynhYauHBQMCorpt8T1N26ajAYbQ9oSYvQCZUC1YSQGsD48ykkLxLv?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[[Quora] Which are the different usage fields of machine learning, deep learning and neural networks?]]></title><description><![CDATA[First things first, let’s define machine learning, deep learning, and neural networks. Machine learning algorithms are a class of algorithms that improve in performance with experience (more data). Neural]]></description><link>http://direct.ecency.com/ai/@kasperfred/quora-which-are-the-different-usage-fields-of-machine-learning-deep-learning-and-neural-networks</link><guid isPermaLink="true">http://direct.ecency.com/ai/@kasperfred/quora-which-are-the-different-usage-fields-of-machine-learning-deep-learning-and-neural-networks</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[kasperfred]]></dc:creator><pubDate>Sun, 10 Sep 2017 07:03:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/Zskj9C56UondJxcjVaienfhVaNb7LJVWG6e787gZnnfwLhHSp6WJmcDfZvUdYfi1gkx7EYzhicAkWCUa1muGK3PNCKWNaoEnoEj2DPqTe83tNRYXKf2e?format=match&amp;mode=fit" length="0" type="false"/></item></channel></rss>