dr_domi/udlbook
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #200 from tomheaton/dynamic

Browse Source 
website dynamic data
This commit is contained in:
udlbook
2024-06-19 09:08:12 -04:00
committed by GitHub
parent 7d6ac5e34f 796f17ed90
commit e850676722
33 changed files with 1506 additions and 10365 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
.prettierignore
View File

@@ -1,7 +1,7 @@
# ignore these directories when formatting the repo
Blogs
CM20315
CM20315_2023
Notebooks
PDFFigures
Slides
/Blogs
/CM20315
/CM20315_2023
/Notebooks
/PDFFigures
/Slides

BIN
build/NMI_Review.pdf
View File

Binary file not shown.

26
build/asset-manifest.json
View File

@@ -1,26 +0,0 @@
{
"files": {
"main.css": "/udlbook/static/css/main.e52d581a.chunk.css",
"main.js": "/udlbook/static/js/main.a803036c.chunk.js",
"main.js.map": "/udlbook/static/js/main.a803036c.chunk.js.map",
"runtime-main.js": "/udlbook/static/js/runtime-main.2b958dff.js",
"runtime-main.js.map": "/udlbook/static/js/runtime-main.2b958dff.js.map",
"static/js/2.e26c66d2.chunk.js": "/udlbook/static/js/2.e26c66d2.chunk.js",
"static/js/2.e26c66d2.chunk.js.map": "/udlbook/static/js/2.e26c66d2.chunk.js.map",
"index.html": "/udlbook/index.html",
"static/css/main.e52d581a.chunk.css.map": "/udlbook/static/css/main.e52d581a.chunk.css.map",
"static/js/2.e26c66d2.chunk.js.LICENSE.txt": "/udlbook/static/js/2.e26c66d2.chunk.js.LICENSE.txt",
"static/media/F23.prince.learning.turquoise.af513a4e.jpg": "/udlbook/static/media/F23.prince.learning.turquoise.af513a4e.jpg",
"static/media/coding.e33bff69.svg": "/udlbook/static/media/coding.e33bff69.svg",
"static/media/instructor.956f72a0.svg": "/udlbook/static/media/instructor.956f72a0.svg",
"static/media/media.9f345230.svg": "/udlbook/static/media/media.9f345230.svg",
"static/media/more.306a6229.svg": "/udlbook/static/media/more.306a6229.svg",
"static/media/square-x-twitter.a2a6cc67.svg": "/udlbook/static/media/square-x-twitter.a2a6cc67.svg"
},
"entrypoints": [
"static/js/runtime-main.2b958dff.js",
"static/js/2.e26c66d2.chunk.js",
"static/css/main.e52d581a.chunk.css",
"static/js/main.a803036c.chunk.js"
]
}

BIN
build/favicon.ico
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 15 KiB

1
build/index.html
View File

@@ -1 +0,0 @@
<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/udlbook/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Web site created using create-react-app"/><link rel="apple-touch-icon" href="/udlbook/logo192.png"/><link rel="manifest" href="/udlbook/manifest.json"/><link rel="preconnect" href="https://fonts.googleapis.com"><link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=Encode+Sans+Expanded:wght@400;700&display=swap" rel="stylesheet"><title>Understanding Deep Learning</title><link href="/udlbook/static/css/main.e52d581a.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function t(t){for(var n,l,i=t[0],a=t[1],c=t[2],p=0,s=[];p<i.length;p++)l=i[p],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in a)Object.prototype.hasOwnProperty.call(a,n)&&(e[n]=a[n]);for(f&&f(t);s.length;)s.shift()();return u.push.apply(u,c||[]),r()}function r(){for(var e,t=0;t<u.length;t++){for(var r=u[t],n=!0,i=1;i<r.length;i++){var a=r[i];0!==o[a]&&(n=!1)}n&&(u.splice(t--,1),e=l(l.s=r[0]))}return e}var n={},o={1:0},u=[];function l(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.m=e,l.c=n,l.d=function(e,t,r){l.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},l.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,t){if(1&t&&(e=l(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(l.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)l.d(r,n,function(t){return e[t]}.bind(null,n));return r},l.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(t,"a",t),t},l.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},l.p="/udlbook/";var i=this["webpackJsonpreact-website-smooth-scroll"]=this["webpackJsonpreact-website-smooth-scroll"]||[],a=i.push.bind(i);i.push=t,i=i.slice();for(var c=0;c<i.length;c++)t(i[c]);var f=a;r()}([])</script><script src="/udlbook/static/js/2.e26c66d2.chunk.js"></script><script src="/udlbook/static/js/main.a803036c.chunk.js"></script></body></html>

BIN
build/logo192.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 5.2 KiB

BIN
build/logo512.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 9.4 KiB

25
build/manifest.json
View File

@@ -1,25 +0,0 @@
{
"short_name": "React App",
"name": "Create React App Sample",
"icons": [
{
"src": "favicon.ico",
"sizes": "64x64 32x32 24x24 16x16",
"type": "image/x-icon"
},
{
"src": "logo192.png",
"type": "image/png",
"sizes": "192x192"
},
{
"src": "logo512.png",
"type": "image/png",
"sizes": "512x512"
}
],
"start_url": ".",
"display": "standalone",
"theme_color": "#000000",
"background_color": "#ffffff"
}

3
build/robots.txt
View File

@@ -1,3 +0,0 @@
# https://www.robotstxt.org/robotstxt.html
User-agent: *
Disallow:

2
build/static/css/main.e52d581a.chunk.css
View File

@@ -1,2 +0,0 @@
*{box-sizing:border-box;margin:0;padding:0;font-family:"Encode Sans Expanded",sans-serif}
/*# sourceMappingURL=main.e52d581a.chunk.css.map */

1
build/static/css/main.e52d581a.chunk.css.map
View File

@@ -1 +0,0 @@
{"version":3,"sources":["webpack://src/App.css"],"names":[],"mappings":"AAAA,EACI,qBAAsB,CACtB,QAAS,CACT,SAAW,CACX,6CACJ","file":"main.e52d581a.chunk.css","sourcesContent":["*{\r\n box-sizing: border-box;\r\n margin: 0;\r\n padding: 0 ;\r\n font-family: 'Encode Sans Expanded', sans-serif;\r\n}"]}

3
build/static/js/2.e26c66d2.chunk.js
View File

File diff suppressed because one or more lines are too long

72
build/static/js/2.e26c66d2.chunk.js.LICENSE.txt
View File

@@ -1,72 +0,0 @@
/*
object-assign
(c) Sindre Sorhus
@license MIT
*/
/**
* React Router DOM v6.0.2
*
* Copyright (c) Remix Software Inc.
*
* This source code is licensed under the MIT license found in the
* LICENSE.md file in the root directory of this source tree.
*
* @license MIT
*/
/**
* React Router v6.0.2
*
* Copyright (c) Remix Software Inc.
*
* This source code is licensed under the MIT license found in the
* LICENSE.md file in the root directory of this source tree.
*
* @license MIT
*/
/** @license React v0.20.2
* scheduler.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
/** @license React v16.13.1
* react-is.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
/** @license React v17.0.2
* react-dom.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
/** @license React v17.0.2
* react-jsx-runtime.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
/** @license React v17.0.2
* react.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/

1
build/static/js/2.e26c66d2.chunk.js.map
View File

File diff suppressed because one or more lines are too long

2
build/static/js/main.a803036c.chunk.js
View File

File diff suppressed because one or more lines are too long

1
build/static/js/main.a803036c.chunk.js.map
View File

File diff suppressed because one or more lines are too long

2
build/static/js/runtime-main.2b958dff.js
View File

@@ -1,2 +0,0 @@
!function(e){function t(t){for(var n,l,i=t[0],a=t[1],c=t[2],p=0,s=[];p<i.length;p++)l=i[p],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in a)Object.prototype.hasOwnProperty.call(a,n)&&(e[n]=a[n]);for(f&&f(t);s.length;)s.shift()();return u.push.apply(u,c||[]),r()}function r(){for(var e,t=0;t<u.length;t++){for(var r=u[t],n=!0,i=1;i<r.length;i++){var a=r[i];0!==o[a]&&(n=!1)}n&&(u.splice(t--,1),e=l(l.s=r[0]))}return e}var n={},o={1:0},u=[];function l(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.m=e,l.c=n,l.d=function(e,t,r){l.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},l.r=function(e){"undefined"!==typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,t){if(1&t&&(e=l(e)),8&t)return e;if(4&t&&"object"===typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(l.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)l.d(r,n,function(t){return e[t]}.bind(null,n));return r},l.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(t,"a",t),t},l.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},l.p="/udlbook/";var i=this["webpackJsonpreact-website-smooth-scroll"]=this["webpackJsonpreact-website-smooth-scroll"]||[],a=i.push.bind(i);i.push=t,i=i.slice();for(var c=0;c<i.length;c++)t(i[c]);var f=a;r()}([]);
//# sourceMappingURL=runtime-main.2b958dff.js.map

1
build/static/js/runtime-main.2b958dff.js.map
View File

File diff suppressed because one or more lines are too long

BIN
build/static/media/F23.prince.learning.turquoise.af513a4e.jpg
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 282 KiB

1495
build/static/media/coding.e33bff69.svg
View File

File diff suppressed because it is too large Load Diff
Side by Side

Before

Width:  |  Height:  |  Size: 96 KiB

1908
build/static/media/instructor.956f72a0.svg
View File

File diff suppressed because it is too large Load Diff
Side by Side

Before

Width:  |  Height:  |  Size: 234 KiB

2101
build/static/media/media.9f345230.svg
View File

File diff suppressed because it is too large Load Diff
Side by Side

Before

Width:  |  Height:  |  Size: 138 KiB

2921
build/static/media/more.306a6229.svg
View File

File diff suppressed because it is too large Load Diff
Side by Side

Before

Width:  |  Height:  |  Size: 266 KiB

39
build/static/media/square-x-twitter.a2a6cc67.svg
View File

@@ -1,39 +0,0 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg
height="10"
width="8.75"
viewBox="0 0 448 512"
version="1.1"
id="svg914"
sodipodi:docname="square-x-twitter.svg"
inkscape:version="1.1.2 (b8e25be8, 2022-02-05)"
xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
xmlns="http://www.w3.org/2000/svg"
xmlns:svg="http://www.w3.org/2000/svg">
<defs
id="defs918" />
<sodipodi:namedview
id="namedview916"
pagecolor="#ffffff"
bordercolor="#666666"
borderopacity="1.0"
inkscape:pageshadow="2"
inkscape:pageopacity="0.0"
inkscape:pagecheckerboard="0"
showgrid="false"
inkscape:zoom="65.6"
inkscape:cx="3.8948171"
inkscape:cy="4.5426829"
inkscape:window-width="1296"
inkscape:window-height="906"
inkscape:window-x="0"
inkscape:window-y="38"
inkscape:window-maximized="0"
inkscape:current-layer="svg914" />
<!--!Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.-->
<path
d="M64 32C28.7 32 0 60.7 0 96V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V96c0-35.3-28.7-64-64-64H64zm297.1 84L257.3 234.6 379.4 396H283.8L209 298.1 123.3 396H75.8l111-126.9L69.7 116h98l67.7 89.5L313.6 116h47.5zM323.3 367.6L153.4 142.9H125.1L296.9 367.6h26.3z"
id="path912"
style="fill:#ffffff;fill-opacity:1" />
</svg>
Side by Side

Before

Width:  |  Height:  |  Size: 1.5 KiB

70
src/components/Footer/index.jsx
View File

@@ -1,9 +1,8 @@
import { FaLinkedin } from "react-icons/fa";
import { FaSquareXTwitter } from "react-icons/fa6";
import { animateScroll as scroll } from "react-scroll";
import twitterImg from "../../images/square-x-twitter.svg";
import {
FooterContainer,
FooterImg,
FooterWrap,
SocialIconLink,
SocialIcons,
@@ -13,6 +12,26 @@ import {
WebsiteRights,
} from "./FooterElements";
const images = [
"https://freepik.com/free-vector/hand-coding-concept-illustration_21864184.htm#query=coding&position=17&from_view=search&track=sph&uuid=5896d847-38e4-4cb9-8fe1-103041c7c933",
"https://freepik.com/free-vector/mathematics-concept-illustration_10733824.htm#query=professor&position=13&from_view=search&track=sph&uuid=5b1a188a-64c5-45af-aae2-8573bc1bed3c",
"https://freepik.com/free-vector/content-concept-illustration_7171429.htm#query=media&position=3&from_view=search&track=sph&uuid=c7e35cf2-d85d-4bba-91a6-1cd883dcf153",
"https://freepik.com/free-vector/library-concept-illustration_9148008.htm#query=library&position=40&from_view=search&track=sph&uuid=abecc792-b6b2-4ec0-b318-5e6cc73ba649",
];
const socials = [
{
href: "https://twitter.com/SimonPrinceAI",
icon: FaSquareXTwitter,
alt: "Twitter",
},
{
href: "https://linkedin.com/in/simon-prince-615bb9165/",
icon: FaLinkedin,
alt: "LinkedIn",
},
];
export default function Footer() {
const toggleHome = () => {
scroll.scrollToTop();
@@ -28,41 +47,28 @@ export default function Footer() {
Understanding Deep Learning
</SocialLogo>
<WebsiteRights>
©{new Date().getFullYear()} Simon J.D. Prince
&copy; {new Date().getFullYear()} Simon J.D. Prince
</WebsiteRights>
<WebsiteRights>
Images by StorySet on FreePik:{" "}
<a href="https://www.freepik.com/free-vector/hand-coding-concept-illustration_21864184.htm#query=coding&position=17&from_view=search&track=sph&uuid=5896d847-38e4-4cb9-8fe1-103041c7c933">
{" "}
[1]{" "}
</a>{" "}
<a href="https://www.freepik.com/free-vector/mathematics-concept-illustration_10733824.htm#query=professor&position=13&from_view=search&track=sph&uuid=5b1a188a-64c5-45af-aae2-8573bc1bed3c">
[2]
</a>{" "}
<a href="https://www.freepik.com/free-vector/content-concept-illustration_7171429.htm#query=media&position=3&from_view=search&track=sph&uuid=c7e35cf2-d85d-4bba-91a6-1cd883dcf153">
{" "}
[3]
</a>{" "}
<a href="https://www.freepik.com/free-vector/library-concept-illustration_9148008.htm#query=library&position=40&from_view=search&track=sph&uuid=abecc792-b6b2-4ec0-b318-5e6cc73ba649">
{" "}
[4]
</a>
{images.map((image, index) => (
<a key={index} href={image}>
[{index + 1}]
</a>
))}
</WebsiteRights>
<SocialIcons>
<SocialIconLink
href="https://twitter.com/SimonPrinceAI"
target="_blank"
aria-label="Twitter"
>
<FooterImg src={twitterImg} alt="twitter" />
</SocialIconLink>
<SocialIconLink
href="https://www.linkedin.com/in/simon-prince-615bb9165/"
target="_blank"
aria-label="LinkedIn"
>
<FaLinkedin />
</SocialIconLink>
{socials.map((social, index) => (
<SocialIconLink
key={index}
href={social.href}
target="_blank"
aria-label={social.alt}
alt={social.alt}
>
<social.icon />
</SocialIconLink>
))}
</SocialIcons>
</SocialMediaWrap>
</SocialMedia>

4
src/components/HeroSection/index.jsx
View File

@@ -169,7 +169,7 @@ export default function HeroSection() {
<HeroFollowBlock>
Follow me on{" "}
<UDLLink href="https://twitter.com/SimonPrinceAI">Twitter</UDLLink> or{" "}
<UDLLink href="https://www.linkedin.com/in/simon-prince-615bb9165/">
<UDLLink href="https://linkedin.com/in/simon-prince-615bb9165/">
LinkedIn
</UDLLink>{" "}
for updates.
@@ -177,7 +177,7 @@ export default function HeroSection() {
</HeroColumn1>
<HeroColumn2>
<HeroImgWrap>
<Img src={img} alt="book cover" />
<Img src={img} alt="UDL Book" />
</HeroImgWrap>
<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v4.0.1/UnderstandingDeepLearning_05_27_24_C.pdf">
Download full pdf (27 May 2024)

686
src/components/Instructors/index.jsx
View File

@@ -16,6 +16,240 @@ import {
TopLine,
} from "./InstructorsElements";
const fullSlides = [
{
text: "Introduction",
link: "https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m",
},
{
text: "Supervised Learning",
link: "https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub",
},
{
text: "Shallow Neural Networks",
link: "https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7",
},
{
text: "Deep Neural Networks",
link: "https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1",
},
{
text: "Loss Functions",
link: "https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH",
},
{
text: "Fitting Models",
link: "https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW",
},
{
text: "Computing Gradients",
link: "https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK",
},
{
text: "Initialization",
link: "https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d",
},
{
text: "Performance",
link: "https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl",
},
{
text: "Regularization",
link: "https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood",
},
{
text: "Convolutional Networks",
link: "https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0",
},
{
text: "Image Generation",
link: "https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs",
},
{
text: "Transformers and LLMs",
link: "https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr",
},
];
const figures = [
{
text: "Introduction",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv",
pptx: "https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Supervised learning",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla",
pptx: "https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Shallow neural networks",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI",
pptx: "https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Deep neural networks",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa",
pptx: "https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Loss functions",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ",
pptx: "https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Training models",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK",
pptx: "https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Gradients and initialization",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs",
pptx: "https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Measuring performance",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx",
pptx: "https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Regularization",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0",
pptx: "https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Convolutional networks",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9",
pptx: "https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Residual networks",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1",
pptx: "https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Transformers",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd",
pptx: "https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Graph neural networks",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe",
pptx: "https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Unsupervised learning",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM",
pptx: "https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "GANs",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL",
pptx: "https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Normalizing flows",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7",
pptx: "https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Variational autoencoders",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3",
pptx: "https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Diffusion models",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r",
pptx: "https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Deep reinforcement learning",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0",
pptx: "https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Why does deep learning work?",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3",
pptx: "https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Deep learning and ethics",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU",
pptx: "https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
{
text: "Appendices",
links: {
pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip",
svg: "https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l",
pptx: "https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
},
},
];
export default function InstructorsSection() {
return (
<>
@@ -34,7 +268,7 @@ export default function InstructorsSection() {
</Column1>
<Column2>
<ImgWrap>
<Img src={img} alt="Car" />
<Img src={img} alt="Instructor" />
</ImgWrap>
</Column2>
</InstructorsRow>
@@ -52,84 +286,14 @@ export default function InstructorsSection() {
</InstructorsContent>
<InstructorsContent>
<ol>
<li>
Introduction{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">
PPTX
</InstructorsLink>
</li>
<li>
Supervised Learning{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">
PPTX
</InstructorsLink>
</li>
<li>
Shallow Neural Networks{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">
PPTX
</InstructorsLink>
</li>
<li>
Deep Neural Networks{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">
PPTX
</InstructorsLink>
</li>
<li>
Loss Functions{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">
PPTX
</InstructorsLink>
</li>
<li>
Fitting Models{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">
PPTX
</InstructorsLink>
</li>
<li>
Computing Gradients{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">
PPTX
</InstructorsLink>
</li>
<li>
Initialization{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">
PPTX
</InstructorsLink>
</li>
<li>
Performance{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">
PPTX
</InstructorsLink>
</li>
<li>
Regularization{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">
PPTX
</InstructorsLink>
</li>
<li>
Convolutional Networks{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">
PPTX
</InstructorsLink>
</li>
<li>
Image Generation{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">
PPTX
</InstructorsLink>
</li>
<li>
Transformers and LLMs{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">
PPTX
</InstructorsLink>
</li>
{fullSlides.map((slide, index) => (
<li key={index}>
{slide.text}{" "}
<InstructorsLink href={slide.link}>
PPTX
</InstructorsLink>
</li>
))}
</ol>
</InstructorsContent>
</Column1>
@@ -137,353 +301,23 @@ export default function InstructorsSection() {
<TopLine>Figures</TopLine>
<InstructorsContent>
<ol>
<li>
{" "}
Introduction:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX{" "}
</InstructorsLink>
</li>
<li>
{" "}
Supervised learning:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Shallow neural networks:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Deep neural networks:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa">
{" "}
SVG
</InstructorsLink>
/
<InstructorsLink href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Loss functions:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Training models:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Gradients and initialization:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Measuring performance:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Regularization:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Convolutional networks:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Residual networks:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Transformers:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Graph neural networks:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Unsupervised learning:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
GANs:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Normalizing flows:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Variational autoencoders:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Diffusion models:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r">
{" "}
SVG
</InstructorsLink>{" "}
/
<InstructorsLink href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Deep reinforcement learning:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX{" "}
</InstructorsLink>
</li>
<li>
{" "}
Why does deep learning work?:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3">
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Deep learning and ethics:{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
<li>
{" "}
Appendices -{" "}
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
PPTX
</InstructorsLink>
</li>
{figures.map((figure, index) => (
<li key={index}>
{figure.text}:{" "}
<InstructorsLink href={figure.links.pdf}>
PDF
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href={figure.links.svg}>
{" "}
SVG
</InstructorsLink>{" "}
/{" "}
<InstructorsLink href={figure.links.pptx}>
PPTX{" "}
</InstructorsLink>
</li>
))}
</ol>
</InstructorsContent>
<InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">

41
src/components/Media/index.jsx
View File

@@ -17,6 +17,19 @@ import {
VideoFrame,
} from "./MediaElements";
const interviews = [
{
href: "https://www.borealisai.com/news/understanding-deep-learning/",
text: "Borealis AI",
linkText: "interview",
},
{
href: "https://shepherd.com/best-books/machine-learning-and-deep-neural-networks",
text: "Shepherd ML book",
linkText: "recommendations",
},
];
export default function MediaSection() {
return (
<>
@@ -27,7 +40,6 @@ export default function MediaSection() {
<TextWrapper>
<TopLine>Media</TopLine>
<Heading lightText={true}>
{" "}
Reviews, videos, podcasts, interviews
</Heading>
<Subtitle darkText={false}>
@@ -37,7 +49,7 @@ export default function MediaSection() {
</Column1>
<Column2>
<ImgWrap>
<Img src={img} alt="Car" />
<Img src={img} alt="Media" />
</ImgWrap>
</Column2>
</MediaRow>
@@ -75,9 +87,9 @@ export default function MediaSection() {
<Column1>
<TopLine>Reviews</TopLine>
<MediaContent>
{/* TODO: add dynamic rendering for reviews */}
<ul>
<li>
{" "}
Nature Machine Intelligence{" "}
<MediaLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">
{" "}
@@ -88,9 +100,7 @@ export default function MediaSection() {
Ge Wang
</MediaLink>
</li>
<li>
{" "}
Amazon{" "}
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
reviews
@@ -110,7 +120,6 @@ export default function MediaSection() {
by Vishal V.
</li>
<li>
{" "}
Amazon{" "}
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
reviews
@@ -136,18 +145,14 @@ export default function MediaSection() {
<TopLine>Interviews</TopLine>
<MediaContent>
<ul>
<li>
Borealis AI{" "}
<MediaLink href="https://www.borealisai.com/news/understanding-deep-learning/">
interview
</MediaLink>
</li>
<li>
Shepherd ML book{" "}
<MediaLink href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">
recommendations
</MediaLink>
</li>
{interviews.map((interview, index) => (
<li key={index}>
{interview.text}{" "}
<MediaLink href={interview.href}>
{interview.linkText}
</MediaLink>
</li>
))}
</ul>
</MediaContent>
</Column2>

1877
src/components/More/index.jsx
View File

File diff suppressed because it is too large Load Diff

55
src/components/Notebooks/NotebookElements.jsx
View File

@@ -3,12 +3,12 @@ import styled from "styled-components";
export const NotebookContainer = styled.div`
color: #fff;
/* background: #f9f9f9; */
background: ${({lightBg}) => (lightBg ? '#f9f9f9': '#010606')};
background: ${({ lightBg }) => (lightBg ? "#f9f9f9" : "#010606")};
@media screen and (max-width: 768px) {
padding: 100px 0;
}
`
`;
export const NotebookWrapper = styled.div`
display: grid;
@@ -20,18 +20,19 @@ export const NotebookWrapper = styled.div`
margin-left: auto;
padding: 0 24px;
justify-content: center;
`
`;
export const NotebookRow = styled.div`
display: grid;
grid-auto-columns: minmax(auto, 1fr);
align-items: center;
grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
@media screen and (max-width: 768px){
grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
@media screen and (max-width: 768px) {
grid-template-areas: ${({ imgStart }) =>
imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
}
`
`;
export const Column1 = styled.p`
margin-bottom: 15px;
@@ -45,7 +46,7 @@ export const Column1 = styled.p`
@media screen and (max-width: 768px) {
font-size: 10px;
}
`
`;
export const Column2 = styled.p`
margin-bottom: 15px;
@@ -59,13 +60,13 @@ export const Column2 = styled.p`
@media screen and (max-width: 768px) {
font-size: 10px;
}
`
`;
export const TextWrapper = styled.div`
max-width: 540px;
padding-top: 0;
padding-bottom: 0;
`
`;
export const TopLine = styled.p`
color: #57c6d1;
@@ -75,37 +76,37 @@ export const TopLine = styled.p`
letter-spacing: 1.4px;
text-transform: uppercase;
margin-bottom: 16px;
`
`;
export const Heading= styled.h1`
export const Heading = styled.h1`
margin-bottom: 24px;
font-size: 48px;
line-height: 1.1;
font-weight: 600;
color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
color: ${({ lightText }) => (lightText ? "#f7f8fa" : "#010606")};
@media screen and (max-width: 480px) {
font-size: 32px;
}
`
`;
export const Subtitle = styled.p`
max-width: 440px;
margin-bottom: 35px;
font-size: 18px;
line-height: 24px;
color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
`
color: ${({ darkText }) => (darkText ? "#010606" : "#fff")};
`;
export const BtnWrap = styled.div`
display: flex;
justify-content: flex-start;
`
`;
export const ImgWrap = styled.div`
max-width: 555px;
height: 100%;
`
`;
export const Img = styled.img`
width: 100%;
@@ -117,28 +118,30 @@ export const Img = styled.img`
export const NBLink = styled.a`
text-decoration: none;
color: #57c6d1;;
color: #57c6d1;
font-weight: 300;
margin: 0 2px;
position: relative;
&:before{
&:before {
position: absolute;
margin: 0 auto;
top: 100%;
left: 0;
width: 100%;
height: 2px;
background-color: #57c6d1;;
content: '';
opacity: .3;
background-color: #57c6d1;
content: "";
opacity: 0.3;
-webkit-transform: scaleX(1);
transition-property: opacity, -webkit-transform;
transition-duration: .3s;
transition-property:
opacity,
-webkit-transform;
transition-duration: 0.3s;
}
&:hover:before {
opacity: 1;
-webkit-transform: scaleX(1.05);
}
`
`;

481
src/components/Notebooks/index.jsx
View File

@@ -1,195 +1,344 @@
import img from '../../images/coding.svg'
import { Column1, Column2, Heading, Img, ImgWrap, NBLink, NotebookContainer, NotebookRow, NotebookWrapper, Subtitle, TextWrapper, TopLine } from './NotebookElements'
import img from "../../images/coding.svg";
import {
Column1,
Column2,
Heading,
Img,
ImgWrap,
NBLink,
NotebookContainer,
NotebookRow,
NotebookWrapper,
Subtitle,
TextWrapper,
TopLine,
} from "./NotebookElements";
const notebooks = [
{
text: "Notebook 1.1 - Background mathematics",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb",
},
{
text: "Notebook 2.1 - Supervised learning",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb",
},
{
text: "Notebook 3.1 - Shallow networks I",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb",
},
{
text: "Notebook 3.2 - Shallow networks II",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb",
},
{
text: "Notebook 3.3 - Shallow network regions",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb",
},
{
text: "Notebook 3.4 - Activation functions",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb",
},
{
text: "Notebook 4.1 - Composing networks",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb",
},
{
text: "Notebook 4.2 - Clipping functions",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb",
},
{
text: "Notebook 4.3 - Deep networks",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb",
},
{
text: "Notebook 5.1 - Least squares loss",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb",
},
{
text: "Notebook 5.2 - Binary cross-entropy loss",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb",
},
{
text: "Notebook 5.3 - Multiclass cross-entropy loss",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb",
},
{
text: "Notebook 6.1 - Line search",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb",
},
{
text: "Notebook 6.2 - Gradient descent",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb",
},
{
text: "Notebook 6.3 - Stochastic gradient descent",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb",
},
{
text: "Notebook 6.4 - Momentum",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb",
},
{
text: "Notebook 6.5 - Adam",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb",
},
{
text: "Notebook 7.1 - Backpropagation in toy model",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb",
},
{
text: "Notebook 7.2 - Backpropagation",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb",
},
{
text: "Notebook 7.3 - Initialization",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb",
},
{
text: "Notebook 8.1 - MNIST-1D performance",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb",
},
{
text: "Notebook 8.2 - Bias-variance trade-off",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb",
},
{
text: "Notebook 8.3 - Double descent",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb",
},
{
text: "Notebook 8.4 - High-dimensional spaces",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb",
},
{
text: "Notebook 9.1 - L2 regularization",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb",
},
{
text: "Notebook 9.2 - Implicit regularization",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb",
},
{
text: "Notebook 9.3 - Ensembling",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb",
},
{
text: "Notebook 9.4 - Bayesian approach",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb",
},
{
text: "Notebook 9.5 - Augmentation",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb",
},
{
text: "Notebook 10.1 - 1D convolution",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb",
},
{
text: "Notebook 10.2 - Convolution for MNIST-1D",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb",
},
{
text: "Notebook 10.3 - 2D convolution",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb",
},
{
text: "Notebook 10.4 - Downsampling & upsampling",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb",
},
{
text: "Notebook 10.5 - Convolution for MNIST",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb",
},
{
text: "Notebook 11.1 - Shattered gradients",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb",
},
{
text: "Notebook 11.2 - Residual networks",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb",
},
{
text: "Notebook 11.3 - Batch normalization",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb",
},
{
text: "Notebook 12.1 - Self-attention",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb",
},
{
text: "Notebook 12.2 - Multi-head self-attention",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb",
},
{
text: "Notebook 12.3 - Tokenization",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb",
},
{
text: "Notebook 12.4 - Decoding strategies",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb",
},
{
text: "Notebook 13.1 - Encoding graphs",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb",
},
{
text: "Notebook 13.2 - Graph classification",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb",
},
{
text: "Notebook 13.3 - Neighborhood sampling",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb",
},
{
text: "Notebook 13.4 - Graph attention",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb",
},
{
text: "Notebook 15.1 - GAN toy example",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb",
},
{
text: "Notebook 15.2 - Wasserstein distance",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb",
},
{
text: "Notebook 16.1 - 1D normalizing flows",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb",
},
{
text: "Notebook 16.2 - Autoregressive flows",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb",
},
{
text: "Notebook 16.3 - Contraction mappings",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb",
},
{
text: "Notebook 17.1 - Latent variable models",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb",
},
{
text: "Notebook 17.2 - Reparameterization trick",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb",
},
{
text: "Notebook 17.3 - Importance sampling",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb",
},
{
text: "Notebook 18.1 - Diffusion encoder",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb",
},
{
text: "Notebook 18.2 - 1D diffusion model",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb",
},
{
text: "Notebook 18.3 - Reparameterized model",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb",
},
{
text: "Notebook 18.4 - Families of diffusion models",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb",
},
{
text: "Notebook 19.1 - Markov decision processes",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb",
},
{
text: "Notebook 19.2 - Dynamic programming",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb",
},
{
text: "Notebook 19.3 - Monte-Carlo methods",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb",
},
{
text: "Notebook 19.4 - Temporal difference methods",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb",
},
{
text: "Notebook 19.5 - Control variates",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb",
},
{
text: "Notebook 20.1 - Random data",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb",
},
{
text: "Notebook 20.2 - Full-batch gradient descent",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb",
},
{
text: "Notebook 20.3 - Lottery tickets",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb",
},
{
text: "Notebook 20.4 - Adversarial attacks",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb",
},
{
text: "Notebook 21.1 - Bias mitigation",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb",
},
{
text: "Notebook 21.2 - Explainability",
link: "https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb",
},
];
export default function NotebookSection() {
return (
<>
<NotebookContainer lightBg={false} id='Notebooks'>
<NotebookContainer lightBg={false} id="Notebooks">
<NotebookWrapper>
<NotebookRow imgStart={true}>
<Column1>
<TextWrapper>
<TopLine>Coding exercises</TopLine>
<Heading lightText={true}>Python notebooks covering the whole text</Heading>
<Subtitle darkText={false}>Sixty eight python notebook exercises with missing code to fill in based on the text</Subtitle>
<Heading lightText={true}>
Python notebooks covering the whole text
</Heading>
<Subtitle darkText={false}>
Sixty eight python notebook exercises with missing code to fill
in based on the text
</Subtitle>
</TextWrapper>
</Column1>
<Column2>
<ImgWrap>
<Img src={img} alt='Car'/>
<Img src={img} alt="Coding" />
</ImgWrap>
</Column2>
</NotebookRow>
<NotebookRow>
<Column1>
<ul>
<li> Notebook 1.1 - Background mathematics: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</NBLink>
</li>
<li> Notebook 2.1 - Supervised learning: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</NBLink>
</li>
<li> Notebook 3.1 - Shallow networks I: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 3.2 - Shallow networks II: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 3.3 - Shallow network regions: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 3.4 - Activation functions: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 4.1 - Composing networks: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 4.2 - Clipping functions: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 4.3 - Deep networks: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 5.1 - Least squares loss: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 5.2 - Binary cross-entropy loss: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 5.3 - Multiclass cross-entropy loss: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 6.1 - Line search: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 6.2 - Gradient descent: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 6.3 - Stochastic gradient descent: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 6.4 - Momentum: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 6.5 - Adam: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 7.1 - Backpropagation in toy model: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 7.2 - Backpropagation: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 7.3 - Initialization: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 8.1 - MNIST-1D performance: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 8.2 - Bias-variance trade-off: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 8.3 - Double descent: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 8.4 - High-dimensional spaces: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 9.1 - L2 regularization: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 9.2 - Implicit regularization: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 9.3 - Ensembling: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 9.4 - Bayesian approach: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 9.5 - Augmentation <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 10.1 - 1D convolution: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 10.2 - Convolution for MNIST-1D: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 10.3 - 2D convolution: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 10.4 - Downsampling & upsampling: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 10.5 - Convolution for MNIST: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </NBLink>
</li>
{/* render first half of notebooks*/}
{notebooks.slice(0, notebooks.length / 2).map((notebook, index) => (
<li key={index}>
{notebook.text}:{" "}
<NBLink href={notebook.link}>ipynb/colab</NBLink>
</li>
))}
</ul>
</Column1>
<Column2>
<ul>
<li> Notebook 11.1 - Shattered gradients: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 11.2 - Residual networks: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 11.3 - Batch normalization: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 12.1 - Self-attention: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 12.2 - Multi-head self-attention: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 12.3 - Tokenization: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 12.4 - Decoding strategies: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 13.1 - Encoding graphs: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 13.2 - Graph classification : <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 13.3 - Neighborhood sampling: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 13.4 - Graph attention: <NBLink
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </NBLink>
</li>
<li> Notebook 15.1 - GAN toy example: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 15.2 - Wasserstein distance: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 16.1 - 1D normalizing flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 16.2 - Autoregressive flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 16.3 - Contraction mappings: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 17.1 - Latent variable models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 17.2 - Reparameterization trick: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 17.3 - Importance sampling: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 18.1 - Diffusion encoder: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 18.2 - 1D diffusion model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 18.3 - Reparameterized model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 18.4 - Families of diffusion models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 19.1 - Markov decision processes: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 19.2 - Dynamic programming: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 19.3 - Monte-Carlo methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 19.4 - Temporal difference methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 19.5 - Control variates: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 20.1 - Random data: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 20.2 - Full-batch gradient descent: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 20.3 - Lottery tickets: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 20.4 - Adversarial attacks: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 21.1 - Bias mitigation: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </NBLink></li>
<li> Notebook 21.2 - Explainability: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </NBLink></li>
<ul>
{/* render second half of notebooks*/}
{notebooks.slice(notebooks.length / 2).map((notebook, index) => (
<li key={index}>
{notebook.text}:{" "}
<NBLink href={notebook.link}>ipynb/colab</NBLink>
</li>
))}
</ul>
</Column2>
</NotebookRow>
</NotebookWrapper>
</NotebookContainer>
</>
)
);
}

39
src/images/square-x-twitter.svg
View File

@@ -1,39 +0,0 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg
height="10"
width="8.75"
viewBox="0 0 448 512"
version="1.1"
id="svg914"
sodipodi:docname="square-x-twitter.svg"
inkscape:version="1.1.2 (b8e25be8, 2022-02-05)"
xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
xmlns="http://www.w3.org/2000/svg"
xmlns:svg="http://www.w3.org/2000/svg">
<defs
id="defs918" />
<sodipodi:namedview
id="namedview916"
pagecolor="#ffffff"
bordercolor="#666666"
borderopacity="1.0"
inkscape:pageshadow="2"
inkscape:pageopacity="0.0"
inkscape:pagecheckerboard="0"
showgrid="false"
inkscape:zoom="65.6"
inkscape:cx="3.8948171"
inkscape:cy="4.5426829"
inkscape:window-width="1296"
inkscape:window-height="906"
inkscape:window-x="0"
inkscape:window-y="38"
inkscape:window-maximized="0"
inkscape:current-layer="svg914" />
<!--!Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.-->
<path
d="M64 32C28.7 32 0 60.7 0 96V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V96c0-35.3-28.7-64-64-64H64zm297.1 84L257.3 234.6 379.4 396H283.8L209 298.1 123.3 396H75.8l111-126.9L69.7 116h98l67.7 89.5L313.6 116h47.5zM323.3 367.6L153.4 142.9H125.1L296.9 367.6h26.3z"
id="path912"
style="fill:#ffffff;fill-opacity:1" />
</svg>
Side by Side

Before

Width:  |  Height:  |  Size: 1.5 KiB

2
src/index.jsx
View File

@@ -1,5 +1,5 @@
import React from "react";
import ReactDOM from "react-dom";
import ReactDOM from "react-dom/client";
import App from "./App";
import "./styles/globals.css";