{"version":"1.0","provider_name":"Phil Schatzmann","provider_url":"https:\/\/www.pschatzmann.ch\/home","author_name":"pschatzmann","author_url":"https:\/\/www.pschatzmann.ch\/home\/author\/pschatzmann\/","title":"Deeplearning4j - Recurrent Neural Networks (RNN) - Phil Schatzmann","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"eesLsoSR36\"><a href=\"https:\/\/www.pschatzmann.ch\/home\/2018\/09\/24\/deeplearningforj-recurrent-neural-networks-rnn\/\">Deeplearning4j &#8211; Recurrent Neural Networks (RNN)<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.pschatzmann.ch\/home\/2018\/09\/24\/deeplearningforj-recurrent-neural-networks-rnn\/embed\/#?secret=eesLsoSR36\" width=\"600\" height=\"338\" title=\"&#8220;Deeplearning4j &#8211; Recurrent Neural Networks (RNN)&#8221; &#8212; Phil Schatzmann\" data-secret=\"eesLsoSR36\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/www.pschatzmann.ch\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","thumbnail_url":"https:\/\/www.pschatzmann.ch\/wp-content\/uploads\/2018\/09\/score.png","thumbnail_width":544,"thumbnail_height":389,"description":"A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of [&hellip;]"}