Datasets:

SALT-NLP
/

Design2Code-HARD

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<style>
	/*
	body {
	font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";
	line-height: 1.5;
	color: rgb(36, 41, 47);
	font-size: 16px;
	line-height: 24px;
	}

	h1, h2 {
	font-weight: 600;
	border-bottom-color: rgb(216, 222, 228);
	border-bottom-style: solid;
	border-bottom-width: 1px;
	margin-top: 24px;
	}

	h1 {
	font-size: 32px;
	line-height: 40px;
	padding-bottom: 9.6px;
	}

	h2 {
	font-size: 24px;
	line-height: 30px;
	padding-bottom: 7.2px;
	}

	table {
	border-collapse: collapse;
	border-color: rgb(128, 128, 128);
	}

	table, tr {
	border-top: 1px solid hsla(210,18%,87%,1);
	}

	tbody {
	display: table-row-group;
	vertical-align: middle;
	border-color: inherit;
	}
	*/


	body {
	box-sizing: border-box;
	min-width: 200px;
	max-width: 1110px;
	margin: 0 auto;
	padding: 45px;
	}

	@media (prefers-color-scheme: dark) {
	body {
	background-color: #0d1117;
	}
	}


	.github-fork-ribbon:before {
	background-color: #121612;
	}

	.banner-image {
	border-radius: 50px;
	}
	</style>
	<!-- Google tag (gtag.js) -->
	<meta charset="utf-8">
	<title>
	Animated AI
	</title>
	<meta content="width=device-width, initial-scale=1.0" name="viewport">
	</head>
	<body>
	<article class="markdown-body">
	<h1>
	<div>
	<img class="banner-image" src="rick.jpg" width="100%">
	</div>
	</h1>
	<p>
	I'm creating animations and instructional videos about neural networks.
	Check out my
	<a>
	Patreon
	</a>
	and
	<a>
	YouTube channel
	</a>
	!
	</p>
	<h2>
	Convolution
	</h2>
	<h3>
	The Basic Algorithm
	</h3>
	Watch the companion YouTube video:
	<a>
	Fundamental Algorithm of Convolution in Neural Networks
	</a>
	.
	<img alt="The process of convolution in neural networks with a 3x3 kernel size" height="auto" src="rick.jpg" width="960">
	<h3>
	Padding
	</h3>
	Companion video:
	<a>
	Convolution Padding - Neural Networks
	</a>
	<table>
	<tbody>
	<tr>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size and a padding of 'SAME', i.e., 1 pixel on the top, bottom, left, and right." height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	No Padding AKA "Valid"
	</td>
	<td>
	[1,1,1,1] Padding AKA "Same"
	</td>
	</tr>
	</tbody>
	</table>
	<h3>
	Stride
	</h3>
	Companion video:
	<a>
	Stride - Convolution in Neural Networks
	</a>
	<table>
	<thead>
	<tr>
	<th>
	Stride 1
	</th>
	<th>
	Stride 2
	</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size and a stride of 2x2" height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	No Padding AKA "Valid"; Stride of 1
	</td>
	<td>
	No Padding AKA "Valid"; Stride of 2
	</td>
	</tr>
	<tr>
	<td>
	<img height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size, a stride of 2x2, and a padding of 'SAME', i.e., 1 pixel on the top, bottom, left, and right." height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	</tbody>
	</table>
	<h3>
	Groups, Depthwise, and Depthwise-Separable
	</h3>
	Watch the companion YouTube video:
	<a>
	Groups, Depthwise, and Depthwise-Separable Convolution (Neural Networks)
	</a>
	.
	<table>
	<tbody>
	<tr>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size and 2 groups" height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	1 Group
	</td>
	<td>
	2 Groups
	</td>
	</tr>
	<tr>
	<td>
	<img alt="The process of convolution in neural networks with a 3x3 kernel size and 8 groups making it a depthwise convolution" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="A depthwise convolution layer followed by a pointwise convolution layer making the combined process a depthwise-separable convolution" height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	Depthwise (8 Groups)
	</td>
	<td>
	Depthwise-separable (8 Groups followed by pointwise)
	</td>
	</tr>
	</tbody>
	</table>
	<h2 id="pixel-shuffle">
	Pixel Shuffle
	</h2>
	Watch the companion YouTube video:
	<a>
	Pixel Shuffle - Changing Resolution with Style
	</a>
	<h3>
	2x2 Block Size
	</h3>
	<table>
	<tbody>
	<tr>
	<td>
	<img alt="Pixel shuffle animation in neural networks with a block size of 2x2" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="Pixel unshuffle animation in neural networks with a block size of 2x2" height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	2x2 Pixel Shuffle
	</td>
	<td>
	2x2 Pixel Unshuffle
	</td>
	</tr>
	<tr>
	<td colspan="2">
	<img alt="Pixel shuffle and unshuffle in neural networks with a block size of 2x2 looping animation" height="auto" src="rick.jpg" width="960">
	</td>
	</tr>
	<tr>
	<td colspan="2">
	2x2 Pixel Shuffle/Unshuffle Loop
	</td>
	</tr>
	</tbody>
	</table>
	<h3>
	3x3 Block Size
	</h3>
	<table>
	<tbody>
	<tr>
	<td>
	<img alt="Pixel shuffle animation in neural networks with a block size of 3x3" height="auto" src="rick.jpg" width="480">
	</td>
	<td>
	<img alt="Pixel unshuffle animation in neural networks with a block size of 3x3" height="auto" src="rick.jpg" width="480">
	</td>
	</tr>
	<tr>
	<td>
	3x3 Pixel Shuffle
	</td>
	<td>
	3x3 Pixel Unshuffle
	</td>
	</tr>
	<tr>
	<td colspan="2">
	<img alt="Pixel shuffle and unshuffle in neural networks with a block size of 3x3 looping animation" height="auto" src="rick.jpg" width="960">
	</td>
	</tr>
	<tr>
	<td colspan="2">
	3x3 Pixel Shuffle/Unshuffle Loop
	</td>
	</tr>
	</tbody>
	</table>
	<h2>
	</h2>
	<p>
	Licensed under the
	<a>
	MIT License
	</a>
	</p>
	</article>
	</body>
	</html>