<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Deep-Learning on Quantified Mind</title><link>https://manuel-delverme.github.io/tags/deep-learning/</link><description>Recent content in Deep-Learning on Quantified Mind</description><generator>Hugo -- 0.147.0</generator><language>en-us</language><lastBuildDate>Thu, 14 Jan 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://manuel-delverme.github.io/tags/deep-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Divide et Impera and Neural Networks</title><link>https://manuel-delverme.github.io/posts/constrained-neural-networks/</link><pubDate>Thu, 14 Jan 2021 00:00:00 +0000</pubDate><guid>https://manuel-delverme.github.io/posts/constrained-neural-networks/</guid><description>A survey of approaches to divide the parameter estimation problem for neural networks into simpler sub-problems using constrained optimization.</description></item></channel></rss>