(public).sospep.subjects ~ view article ~ machine-learning-works-spectacularly-well

<h1 style="text-align: center;">machine learning works, math guys don't know why</h1>
<h2>[WHAT]</h2>
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<li>]&nbsp;by __ @ quantamagazine.org -&nbsp;</li>
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<h2>[WHY]</h2>
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<li>]&nbsp;</li>
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<h2>[WHERE]</h2>
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<li><strong>] READ THE FULL ARTICLE</strong></li>
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<li>]&nbsp;<a href="https://www.quantamagazine.org/20151203-big-datas-mathematical-mysteries/" target="_blank">https://www.quantamagazine.org/20151203-big-datas-mathematical-mysteries/</a></li>
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<h2>[WHEN]</h2>
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<li>]</li>
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<h2>[EXAMPLE auto summary by smmry.com]</h2>
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<li>] In mathematical terms, these supervised-learning systems are given a large set of inputs and the corresponding outputs; the goal is for a computer to learn the function that will reliably transform a new input into the correct output.</li>
<li>] To do this, the computer breaks down the mystery function into a number of layers of unknown functions called sigmoid functions.</li>
<li>] These S-shaped functions look like a street-to-curb transition: a smoothened step from one level to another, where the starting level, the height of the step and the width of the transition region are not determined ahead of time.</li>
<li>] Inputs enter the first layer of sigmoid functions, which spits out results that can be combined before being fed into a second layer of sigmoid functions, and so on.</li>
<li>] Decades ago, researchers proved that these networks are universal, meaning that they can generate all possible functions.</li>
<li>] These results assume networks that can have extremely large numbers of layers and of function nodes within each layer.</li>
<li>] Perhaps the personality-test surface can also be described with a simple function, a connection between a number of variables that is significantly smaller than 500.</li>
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<h2>[HOW-TO]</h2>
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<li>]</li>
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<h2>[REFERENCE]</h2>
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<li>] SRC = hn, <a href="https://news.ycombinator.com/item?id=10672276" target="_blank">comments</a></li>
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<h1 style="text-align: center;">&nbsp;</h1>

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