-
Notifications
You must be signed in to change notification settings - Fork 89
/
index.html
251 lines (248 loc) · 9.45 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<title>MLU-Explain</title>
<meta name="description" content="MLU-Explain" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="MLU-Explain" />
<meta
property="og:image"
content="https://mlu-explain.github.io/assets/mluexplain-homepage-ogimage.png"
/>
<meta
property="og:description"
content="Visual explanations of core machine learning concepts."
/>
<meta property="og:image:width" content="1200" />
<meta property="og:image:height" content="600" />
<link rel="icon" href="./assets/mlu_robot.png" />
<link rel="stylesheet" href="css/styles.css" />
<!-- Global site tag (gtag.js) - Google Analytics -->
<script
async
src="https://www.googletagmanager.com/gtag/js?id=G-1FYW57GW3G"
></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag("js", new Date());
gtag("config", "G-1FYW57GW3G");
</script>
</head>
<body>
<main>
<section id="intro">
<div class="wrapper">
<div id="intro-container">
<div id="intro-text">
<span class="intro-icon-container">
<span class="icon">
<svg
width="60"
height="100"
viewBox="0 0 300 180"
fill="none"
xmlns="https://www.w3.org/2000/svg"
id="mlu-icon"
>
<g id="mlu_robot 1" clip-path="url(#clip0)">
<g>
<g id="eyes">
<path
id="Vector"
d="M90.6641 83.1836C96.8828 83.1836 101.941 78.1289 101.941 71.8906V71.8242C101.941 65.5898 96.8945 60.5312 90.6641 60.5312C84.4453 60.5312 79.3828 65.5898 79.3828 71.8242V71.8906C79.3828 78.1289 84.4336 83.1836 90.6641 83.1836Z"
fill="#232F3E"
></path>
<path
id="Vector_2"
d="M143.305 83.1836C149.523 83.1836 154.586 78.1289 154.586 71.8906V71.8242C154.586 65.5898 149.535 60.5312 143.305 60.5312C137.09 60.5312 132.027 65.5898 132.027 71.8242V71.8906C132.027 78.1289 137.078 83.1836 143.305 83.1836Z"
fill="#232F3E"
></path>
</g>
<path
id="Vector_3"
d="M163.586
159.402H173.609V122.641H163.586V159.402Z"
fill="#232F3E"
></path>
<path
id="Vector_4"
d="M60.3594 159.402H70.3867V122.641H60.3594V159.402Z"
fill="#232F3E"
></path>
<g id="Group">
<path
id="Vector_5"
d="M182.16 30.0781H51.8047V10.0234H182.16V30.0781ZM182.16 103.609H51.8047V40.1055H182.16V103.609ZM144.559 168.789H89.4062V113.641H144.559V168.789ZM0 0V10.0234H15.8789V46.7891H25.9023V10.0234H41.7812V113.641H79.3867V178.816H96.9297V215.578H106.957V178.816H127.016V215.578H137.039V178.816H154.586V113.641H192.188V10.0234H233.969V0"
fill="#232F3E"
></path>
</g>
</g>
</g>
<defs>
<clipPath id="clip0">
<rect
width="233.97"
height="215.58"
fill="white"
></rect>
</clipPath>
</defs>
</svg>
</span>
<span class="text"
><h1>MLU-EXPL<span id="ai">AI</span>N</h1></span
>
</span>
<h3 class="subtitle">
Visual explanations of core machine learning concepts
</h3>
<br />
<p>
<a
href="https://aws.amazon.com/machine-learning/mlu/"
id="mlu-link"
>Machine Learning University (<span id="mlu-acronym">MLU</span
>)</a
>
is an education initiative from Amazon designed to teach machine
learning theory and practical application.
<br /><br />
As part of that goal,
<span class="havy">MLU-Explain</span> exists to teach important
machine learning concepts through visual essays in a fun,
informative, and accessible manner.
<br />
</p>
</div>
<div id="image-container">
<img
id="intro-image"
src="./assets/mlu-drawing-transparent.png"
alt="MLU Robot Deriving Beta Coefficient For Least Squares on Whiteboard"
/>
</div>
</div>
</div>
</section>
<section id="articles-section">
<p class="section-segue">Explore Published Articles...</p>
<br />
<br />
<div class="articles-container">
<!-- card -->
<div class="article-card">
<div
class="imgBx"
onclick="location.href='./decision-tree/';"
style="cursor: pointer"
>
<img
src="./assets/thumbnails/thumbnail-decision-tree.jpg"
alt="Decision Tree Title Image"
/>
</div>
<div class="content">
<br />
<p class="article-description">
Explore one of machine learning's most popular supervised
algorithms: the Decision Tree. Learn how the tree makes its
splits, the concepts of Entropy and Information Gain, and why
going too deep is problematic.
</p>
<button class="content-button">
<a href="./decision-tree/">Dive In.</a>
</button>
</div>
</div>
<!-- card end -->
<!-- card -->
<div class="article-card">
<div
class="imgBx"
onclick="location.href='./double-descent/';"
style="cursor: pointer"
>
<img
src="./assets/thumbnails/thumbnail-double-descent.jpg"
alt="Double Descent Title Image"
/>
</div>
<div class="content">
<br />
<p class="article-description">
Meet the double descent phenomenon in modern machine learning:
what it is, how it relates to the bias-variance tradeoff, the
importance of the interpolation regime, and a theory of what
lies behind.
</p>
<button class="content-button">
<a href="./double-descent/">Dive In.</a>
</button>
</div>
</div>
<!-- card end -->
<!-- card -->
<div class="article-card">
<div
class="imgBx"
onclick="location.href='./double-descent2/';"
style="cursor: pointer"
>
<img
src="./assets/thumbnails/thumbnail-double-descent2.jpg"
alt="Double Descent 2 Title Image"
/>
</div>
<div class="content">
<br />
<p class="article-description">
Deepen your understanding of the double descent phenomenon. The
article builds on the cubic spline example introduced in
<span class="bold">Double Descent 1</span>, describing in
mathematical detail what is happening.
</p>
<button class="content-button">
<a href="./double-descent2/">Dive In.</a>
</button>
</div>
</div>
<!-- card end -->
<!-- card -->
<div class="article-card">
<div
class="imgBx"
onclick="location.href='./bias-variance/';"
style="cursor: pointer"
>
<img
src="./assets/thumbnails/thumbnail-bias-variance.jpg"
alt="Bias Variance Title Image"
/>
</div>
<div class="content">
<br />
<p class="article-description">
Understand the tradeoff between under- and over-fitting models,
how it relates to bias and variance, and explore interactive
examples related to LOESS and KNN.
</p>
<button class="content-button">
<a href="./bias-variance/">Dive In.</a>
</button>
</div>
</div>
<!-- card end -->
</div>
</section>
</main>
<br /><br />
<br /><br />
<script src="js/rough-notation.js"></script>
<script src="js/index.js"></script>
</body>
</html>