1.0라이브스마트https://atmokpo.com/wroothttps://atmokpo.com/w/author/root/rich600338<blockquote class="wp-embedded-content" data-secret="MNN8fwvTvI"><a href="https://atmokpo.com/w/36471/">Deep Learning PyTorch Course, RNN Cell Implementation</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://atmokpo.com/w/36471/embed/#?secret=MNN8fwvTvI" width="600" height="338" title="“Deep Learning PyTorch Course, RNN Cell Implementation” — 라이브스마트" data-secret="MNN8fwvTvI" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !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); //# sourceURL=https://atmokpo.com/w/wp-includes/js/wp-embed.min.js </script> This article provides a detailed explanation of one of the core structures of deep learning, the Recurrent Neural Network (RNN), and demonstrates how to implement an RNN cell using PyTorch. RNNs are very useful for processing sequence data and are widely used in various fields such as natural language processing, speech recognition, and stock prediction. … 더 보기 ""https://latex.codecogs.com/svg.latex?h_t%20=%20f(W_h%20h_%7Bt-1%7D%20+%20W_x%20x_t%20+%20b)%2C