udlbook
@@ -1,7 +1,7 @@
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# ignore these directories when formatting the repo
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# ignore these directories when formatting the repo
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||||||
Blogs
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/Blogs
|
||||||
CM20315
|
/CM20315
|
||||||
CM20315_2023
|
/CM20315_2023
|
||||||
Notebooks
|
/Notebooks
|
||||||
PDFFigures
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/PDFFigures
|
||||||
Slides
|
/Slides
|
||||||
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|||||||
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|||||||
{
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||||||
"files": {
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||||||
"main.css": "/udlbook/static/css/main.e52d581a.chunk.css",
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||||||
"main.js": "/udlbook/static/js/main.a803036c.chunk.js",
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||||||
"main.js.map": "/udlbook/static/js/main.a803036c.chunk.js.map",
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|
||||||
"runtime-main.js": "/udlbook/static/js/runtime-main.2b958dff.js",
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|
||||||
"runtime-main.js.map": "/udlbook/static/js/runtime-main.2b958dff.js.map",
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||||||
"static/js/2.e26c66d2.chunk.js": "/udlbook/static/js/2.e26c66d2.chunk.js",
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||||||
"static/js/2.e26c66d2.chunk.js.map": "/udlbook/static/js/2.e26c66d2.chunk.js.map",
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|
||||||
"index.html": "/udlbook/index.html",
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|
||||||
"static/css/main.e52d581a.chunk.css.map": "/udlbook/static/css/main.e52d581a.chunk.css.map",
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|
||||||
"static/js/2.e26c66d2.chunk.js.LICENSE.txt": "/udlbook/static/js/2.e26c66d2.chunk.js.LICENSE.txt",
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|
||||||
"static/media/F23.prince.learning.turquoise.af513a4e.jpg": "/udlbook/static/media/F23.prince.learning.turquoise.af513a4e.jpg",
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|
||||||
"static/media/coding.e33bff69.svg": "/udlbook/static/media/coding.e33bff69.svg",
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||||||
"static/media/instructor.956f72a0.svg": "/udlbook/static/media/instructor.956f72a0.svg",
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||||||
"static/media/media.9f345230.svg": "/udlbook/static/media/media.9f345230.svg",
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||||||
"static/media/more.306a6229.svg": "/udlbook/static/media/more.306a6229.svg",
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|
||||||
"static/media/square-x-twitter.a2a6cc67.svg": "/udlbook/static/media/square-x-twitter.a2a6cc67.svg"
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|
||||||
},
|
|
||||||
"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"
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|
||||||
]
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||||||
}
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||||||
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|
|||||||
<!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>
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|
|||||||
{
|
|
||||||
"short_name": "React App",
|
|
||||||
"name": "Create React App Sample",
|
|
||||||
"icons": [
|
|
||||||
{
|
|
||||||
"src": "favicon.ico",
|
|
||||||
"sizes": "64x64 32x32 24x24 16x16",
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|
||||||
"type": "image/x-icon"
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|
||||||
},
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|
||||||
{
|
|
||||||
"src": "logo192.png",
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|
||||||
"type": "image/png",
|
|
||||||
"sizes": "192x192"
|
|
||||||
},
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|
||||||
{
|
|
||||||
"src": "logo512.png",
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|
||||||
"type": "image/png",
|
|
||||||
"sizes": "512x512"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"start_url": ".",
|
|
||||||
"display": "standalone",
|
|
||||||
"theme_color": "#000000",
|
|
||||||
"background_color": "#ffffff"
|
|
||||||
}
|
|
||||||
@@ -1,3 +0,0 @@
|
|||||||
# https://www.robotstxt.org/robotstxt.html
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|
||||||
User-agent: *
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|
||||||
Disallow:
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||||||
@@ -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 +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}"]}
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|
||||||
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@@ -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.
|
|
||||||
*/
|
|
||||||
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|
|||||||
!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
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|
||||||
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@@ -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>
|
|
||||||
|
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@@ -1,9 +1,8 @@
|
|||||||
import { FaLinkedin } from "react-icons/fa";
|
import { FaLinkedin } from "react-icons/fa";
|
||||||
|
import { FaSquareXTwitter } from "react-icons/fa6";
|
||||||
import { animateScroll as scroll } from "react-scroll";
|
import { animateScroll as scroll } from "react-scroll";
|
||||||
import twitterImg from "../../images/square-x-twitter.svg";
|
|
||||||
import {
|
import {
|
||||||
FooterContainer,
|
FooterContainer,
|
||||||
FooterImg,
|
|
||||||
FooterWrap,
|
FooterWrap,
|
||||||
SocialIconLink,
|
SocialIconLink,
|
||||||
SocialIcons,
|
SocialIcons,
|
||||||
@@ -13,6 +12,26 @@ import {
|
|||||||
WebsiteRights,
|
WebsiteRights,
|
||||||
} from "./FooterElements";
|
} 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() {
|
export default function Footer() {
|
||||||
const toggleHome = () => {
|
const toggleHome = () => {
|
||||||
scroll.scrollToTop();
|
scroll.scrollToTop();
|
||||||
@@ -28,41 +47,28 @@ export default function Footer() {
|
|||||||
Understanding Deep Learning
|
Understanding Deep Learning
|
||||||
</SocialLogo>
|
</SocialLogo>
|
||||||
<WebsiteRights>
|
<WebsiteRights>
|
||||||
©{new Date().getFullYear()} Simon J.D. Prince
|
© {new Date().getFullYear()} Simon J.D. Prince
|
||||||
</WebsiteRights>
|
</WebsiteRights>
|
||||||
<WebsiteRights>
|
<WebsiteRights>
|
||||||
Images by StorySet on FreePik:{" "}
|
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">
|
{images.map((image, index) => (
|
||||||
{" "}
|
<a key={index} href={image}>
|
||||||
[1]{" "}
|
[{index + 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>
|
</a>
|
||||||
|
))}
|
||||||
</WebsiteRights>
|
</WebsiteRights>
|
||||||
<SocialIcons>
|
<SocialIcons>
|
||||||
|
{socials.map((social, index) => (
|
||||||
<SocialIconLink
|
<SocialIconLink
|
||||||
href="https://twitter.com/SimonPrinceAI"
|
key={index}
|
||||||
|
href={social.href}
|
||||||
target="_blank"
|
target="_blank"
|
||||||
aria-label="Twitter"
|
aria-label={social.alt}
|
||||||
|
alt={social.alt}
|
||||||
>
|
>
|
||||||
<FooterImg src={twitterImg} alt="twitter" />
|
<social.icon />
|
||||||
</SocialIconLink>
|
|
||||||
<SocialIconLink
|
|
||||||
href="https://www.linkedin.com/in/simon-prince-615bb9165/"
|
|
||||||
target="_blank"
|
|
||||||
aria-label="LinkedIn"
|
|
||||||
>
|
|
||||||
<FaLinkedin />
|
|
||||||
</SocialIconLink>
|
</SocialIconLink>
|
||||||
|
))}
|
||||||
</SocialIcons>
|
</SocialIcons>
|
||||||
</SocialMediaWrap>
|
</SocialMediaWrap>
|
||||||
</SocialMedia>
|
</SocialMedia>
|
||||||
|
|||||||
@@ -169,7 +169,7 @@ export default function HeroSection() {
|
|||||||
<HeroFollowBlock>
|
<HeroFollowBlock>
|
||||||
Follow me on{" "}
|
Follow me on{" "}
|
||||||
<UDLLink href="https://twitter.com/SimonPrinceAI">Twitter</UDLLink> or{" "}
|
<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
|
LinkedIn
|
||||||
</UDLLink>{" "}
|
</UDLLink>{" "}
|
||||||
for updates.
|
for updates.
|
||||||
@@ -177,7 +177,7 @@ export default function HeroSection() {
|
|||||||
</HeroColumn1>
|
</HeroColumn1>
|
||||||
<HeroColumn2>
|
<HeroColumn2>
|
||||||
<HeroImgWrap>
|
<HeroImgWrap>
|
||||||
<Img src={img} alt="book cover" />
|
<Img src={img} alt="UDL Book" />
|
||||||
</HeroImgWrap>
|
</HeroImgWrap>
|
||||||
<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v4.0.1/UnderstandingDeepLearning_05_27_24_C.pdf">
|
<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v4.0.1/UnderstandingDeepLearning_05_27_24_C.pdf">
|
||||||
Download full pdf (27 May 2024)
|
Download full pdf (27 May 2024)
|
||||||
|
|||||||
@@ -16,6 +16,240 @@ import {
|
|||||||
TopLine,
|
TopLine,
|
||||||
} from "./InstructorsElements";
|
} 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() {
|
export default function InstructorsSection() {
|
||||||
return (
|
return (
|
||||||
<>
|
<>
|
||||||
@@ -34,7 +268,7 @@ export default function InstructorsSection() {
|
|||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
<ImgWrap>
|
<ImgWrap>
|
||||||
<Img src={img} alt="Car" />
|
<Img src={img} alt="Instructor" />
|
||||||
</ImgWrap>
|
</ImgWrap>
|
||||||
</Column2>
|
</Column2>
|
||||||
</InstructorsRow>
|
</InstructorsRow>
|
||||||
@@ -52,84 +286,14 @@ export default function InstructorsSection() {
|
|||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
<ol>
|
<ol>
|
||||||
<li>
|
{fullSlides.map((slide, index) => (
|
||||||
Introduction{" "}
|
<li key={index}>
|
||||||
<InstructorsLink href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">
|
{slide.text}{" "}
|
||||||
PPTX
|
<InstructorsLink href={slide.link}>
|
||||||
</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
|
PPTX
|
||||||
</InstructorsLink>
|
</InstructorsLink>
|
||||||
</li>
|
</li>
|
||||||
|
))}
|
||||||
</ol>
|
</ol>
|
||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
</Column1>
|
</Column1>
|
||||||
@@ -137,353 +301,23 @@ export default function InstructorsSection() {
|
|||||||
<TopLine>Figures</TopLine>
|
<TopLine>Figures</TopLine>
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
<ol>
|
<ol>
|
||||||
<li>
|
{figures.map((figure, index) => (
|
||||||
{" "}
|
<li key={index}>
|
||||||
Introduction:{" "}
|
{figure.text}:{" "}
|
||||||
<InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">
|
<InstructorsLink href={figure.links.pdf}>
|
||||||
PDF
|
PDF
|
||||||
</InstructorsLink>{" "}
|
</InstructorsLink>{" "}
|
||||||
/{" "}
|
/{" "}
|
||||||
<InstructorsLink href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv">
|
<InstructorsLink href={figure.links.svg}>
|
||||||
{" "}
|
{" "}
|
||||||
SVG
|
SVG
|
||||||
</InstructorsLink>{" "}
|
</InstructorsLink>{" "}
|
||||||
/{" "}
|
/{" "}
|
||||||
<InstructorsLink href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
<InstructorsLink href={figure.links.pptx}>
|
||||||
PPTX{" "}
|
PPTX{" "}
|
||||||
</InstructorsLink>
|
</InstructorsLink>
|
||||||
</li>
|
</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
|
|
||||||
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|
|
||||||
</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>{" "}
|
|
||||||
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|
|
||||||
<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">
|
|
||||||
{" "}
|
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||||||
SVG
|
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||||||
</InstructorsLink>{" "}
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||||||
/{" "}
|
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||||||
<InstructorsLink href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
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||||||
PPTX
|
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||||||
</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">
|
|
||||||
{" "}
|
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||||||
SVG
|
|
||||||
</InstructorsLink>{" "}
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||||||
/{" "}
|
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||||||
<InstructorsLink href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
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||||||
PPTX
|
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||||||
</InstructorsLink>
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||||||
</li>
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|
||||||
<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">
|
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||||||
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">
|
|
||||||
{" "}
|
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||||||
SVG
|
|
||||||
</InstructorsLink>{" "}
|
|
||||||
/
|
|
||||||
<InstructorsLink href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
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||||||
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>
|
|
||||||
</ol>
|
</ol>
|
||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
<InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">
|
<InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">
|
||||||
|
|||||||
@@ -17,6 +17,19 @@ import {
|
|||||||
VideoFrame,
|
VideoFrame,
|
||||||
} from "./MediaElements";
|
} 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() {
|
export default function MediaSection() {
|
||||||
return (
|
return (
|
||||||
<>
|
<>
|
||||||
@@ -27,7 +40,6 @@ export default function MediaSection() {
|
|||||||
<TextWrapper>
|
<TextWrapper>
|
||||||
<TopLine>Media</TopLine>
|
<TopLine>Media</TopLine>
|
||||||
<Heading lightText={true}>
|
<Heading lightText={true}>
|
||||||
{" "}
|
|
||||||
Reviews, videos, podcasts, interviews
|
Reviews, videos, podcasts, interviews
|
||||||
</Heading>
|
</Heading>
|
||||||
<Subtitle darkText={false}>
|
<Subtitle darkText={false}>
|
||||||
@@ -37,7 +49,7 @@ export default function MediaSection() {
|
|||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
<ImgWrap>
|
<ImgWrap>
|
||||||
<Img src={img} alt="Car" />
|
<Img src={img} alt="Media" />
|
||||||
</ImgWrap>
|
</ImgWrap>
|
||||||
</Column2>
|
</Column2>
|
||||||
</MediaRow>
|
</MediaRow>
|
||||||
@@ -75,9 +87,9 @@ export default function MediaSection() {
|
|||||||
<Column1>
|
<Column1>
|
||||||
<TopLine>Reviews</TopLine>
|
<TopLine>Reviews</TopLine>
|
||||||
<MediaContent>
|
<MediaContent>
|
||||||
|
{/* TODO: add dynamic rendering for reviews */}
|
||||||
<ul>
|
<ul>
|
||||||
<li>
|
<li>
|
||||||
{" "}
|
|
||||||
Nature Machine Intelligence{" "}
|
Nature Machine Intelligence{" "}
|
||||||
<MediaLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">
|
<MediaLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">
|
||||||
{" "}
|
{" "}
|
||||||
@@ -88,9 +100,7 @@ export default function MediaSection() {
|
|||||||
Ge Wang
|
Ge Wang
|
||||||
</MediaLink>
|
</MediaLink>
|
||||||
</li>
|
</li>
|
||||||
|
|
||||||
<li>
|
<li>
|
||||||
{" "}
|
|
||||||
Amazon{" "}
|
Amazon{" "}
|
||||||
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
|
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
|
||||||
reviews
|
reviews
|
||||||
@@ -110,7 +120,6 @@ export default function MediaSection() {
|
|||||||
by Vishal V.
|
by Vishal V.
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
{" "}
|
|
||||||
Amazon{" "}
|
Amazon{" "}
|
||||||
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
|
<MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
|
||||||
reviews
|
reviews
|
||||||
@@ -136,18 +145,14 @@ export default function MediaSection() {
|
|||||||
<TopLine>Interviews</TopLine>
|
<TopLine>Interviews</TopLine>
|
||||||
<MediaContent>
|
<MediaContent>
|
||||||
<ul>
|
<ul>
|
||||||
<li>
|
{interviews.map((interview, index) => (
|
||||||
Borealis AI{" "}
|
<li key={index}>
|
||||||
<MediaLink href="https://www.borealisai.com/news/understanding-deep-learning/">
|
{interview.text}{" "}
|
||||||
interview
|
<MediaLink href={interview.href}>
|
||||||
</MediaLink>
|
{interview.linkText}
|
||||||
</li>
|
|
||||||
<li>
|
|
||||||
Shepherd ML book{" "}
|
|
||||||
<MediaLink href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">
|
|
||||||
recommendations
|
|
||||||
</MediaLink>
|
</MediaLink>
|
||||||
</li>
|
</li>
|
||||||
|
))}
|
||||||
</ul>
|
</ul>
|
||||||
</MediaContent>
|
</MediaContent>
|
||||||
</Column2>
|
</Column2>
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
@@ -3,12 +3,12 @@ import styled from "styled-components";
|
|||||||
export const NotebookContainer = styled.div`
|
export const NotebookContainer = styled.div`
|
||||||
color: #fff;
|
color: #fff;
|
||||||
/* background: #f9f9f9; */
|
/* background: #f9f9f9; */
|
||||||
background: ${({lightBg}) => (lightBg ? '#f9f9f9': '#010606')};
|
background: ${({ lightBg }) => (lightBg ? "#f9f9f9" : "#010606")};
|
||||||
|
|
||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
padding: 100px 0;
|
padding: 100px 0;
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const NotebookWrapper = styled.div`
|
export const NotebookWrapper = styled.div`
|
||||||
display: grid;
|
display: grid;
|
||||||
@@ -20,7 +20,7 @@ export const NotebookWrapper = styled.div`
|
|||||||
margin-left: auto;
|
margin-left: auto;
|
||||||
padding: 0 24px;
|
padding: 0 24px;
|
||||||
justify-content: center;
|
justify-content: center;
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const NotebookRow = styled.div`
|
export const NotebookRow = styled.div`
|
||||||
display: grid;
|
display: grid;
|
||||||
@@ -29,9 +29,10 @@ export const NotebookRow = styled.div`
|
|||||||
grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
|
grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
|
||||||
|
|
||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
|
grid-template-areas: ${({ imgStart }) =>
|
||||||
|
imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const Column1 = styled.p`
|
export const Column1 = styled.p`
|
||||||
margin-bottom: 15px;
|
margin-bottom: 15px;
|
||||||
@@ -45,7 +46,7 @@ export const Column1 = styled.p`
|
|||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
font-size: 10px;
|
font-size: 10px;
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const Column2 = styled.p`
|
export const Column2 = styled.p`
|
||||||
margin-bottom: 15px;
|
margin-bottom: 15px;
|
||||||
@@ -59,13 +60,13 @@ export const Column2 = styled.p`
|
|||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
font-size: 10px;
|
font-size: 10px;
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const TextWrapper = styled.div`
|
export const TextWrapper = styled.div`
|
||||||
max-width: 540px;
|
max-width: 540px;
|
||||||
padding-top: 0;
|
padding-top: 0;
|
||||||
padding-bottom: 0;
|
padding-bottom: 0;
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const TopLine = styled.p`
|
export const TopLine = styled.p`
|
||||||
color: #57c6d1;
|
color: #57c6d1;
|
||||||
@@ -75,37 +76,37 @@ export const TopLine = styled.p`
|
|||||||
letter-spacing: 1.4px;
|
letter-spacing: 1.4px;
|
||||||
text-transform: uppercase;
|
text-transform: uppercase;
|
||||||
margin-bottom: 16px;
|
margin-bottom: 16px;
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const Heading = styled.h1`
|
export const Heading = styled.h1`
|
||||||
margin-bottom: 24px;
|
margin-bottom: 24px;
|
||||||
font-size: 48px;
|
font-size: 48px;
|
||||||
line-height: 1.1;
|
line-height: 1.1;
|
||||||
font-weight: 600;
|
font-weight: 600;
|
||||||
color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
|
color: ${({ lightText }) => (lightText ? "#f7f8fa" : "#010606")};
|
||||||
|
|
||||||
@media screen and (max-width: 480px) {
|
@media screen and (max-width: 480px) {
|
||||||
font-size: 32px;
|
font-size: 32px;
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const Subtitle = styled.p`
|
export const Subtitle = styled.p`
|
||||||
max-width: 440px;
|
max-width: 440px;
|
||||||
margin-bottom: 35px;
|
margin-bottom: 35px;
|
||||||
font-size: 18px;
|
font-size: 18px;
|
||||||
line-height: 24px;
|
line-height: 24px;
|
||||||
color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
|
color: ${({ darkText }) => (darkText ? "#010606" : "#fff")};
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const BtnWrap = styled.div`
|
export const BtnWrap = styled.div`
|
||||||
display: flex;
|
display: flex;
|
||||||
justify-content: flex-start;
|
justify-content: flex-start;
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const ImgWrap = styled.div`
|
export const ImgWrap = styled.div`
|
||||||
max-width: 555px;
|
max-width: 555px;
|
||||||
height: 100%;
|
height: 100%;
|
||||||
`
|
`;
|
||||||
|
|
||||||
export const Img = styled.img`
|
export const Img = styled.img`
|
||||||
width: 100%;
|
width: 100%;
|
||||||
@@ -117,7 +118,7 @@ export const Img = styled.img`
|
|||||||
|
|
||||||
export const NBLink = styled.a`
|
export const NBLink = styled.a`
|
||||||
text-decoration: none;
|
text-decoration: none;
|
||||||
color: #57c6d1;;
|
color: #57c6d1;
|
||||||
font-weight: 300;
|
font-weight: 300;
|
||||||
margin: 0 2px;
|
margin: 0 2px;
|
||||||
position: relative;
|
position: relative;
|
||||||
@@ -129,16 +130,18 @@ export const NBLink = styled.a`
|
|||||||
left: 0;
|
left: 0;
|
||||||
width: 100%;
|
width: 100%;
|
||||||
height: 2px;
|
height: 2px;
|
||||||
background-color: #57c6d1;;
|
background-color: #57c6d1;
|
||||||
content: '';
|
content: "";
|
||||||
opacity: .3;
|
opacity: 0.3;
|
||||||
-webkit-transform: scaleX(1);
|
-webkit-transform: scaleX(1);
|
||||||
transition-property: opacity, -webkit-transform;
|
transition-property:
|
||||||
transition-duration: .3s;
|
opacity,
|
||||||
|
-webkit-transform;
|
||||||
|
transition-duration: 0.3s;
|
||||||
}
|
}
|
||||||
|
|
||||||
&:hover:before {
|
&:hover:before {
|
||||||
opacity: 1;
|
opacity: 1;
|
||||||
-webkit-transform: scaleX(1.05);
|
-webkit-transform: scaleX(1.05);
|
||||||
}
|
}
|
||||||
`
|
`;
|
||||||
|
|||||||
@@ -1,195 +1,344 @@
|
|||||||
import img from '../../images/coding.svg'
|
import img from "../../images/coding.svg";
|
||||||
import { Column1, Column2, Heading, Img, ImgWrap, NBLink, NotebookContainer, NotebookRow, NotebookWrapper, Subtitle, TextWrapper, TopLine } from './NotebookElements'
|
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() {
|
export default function NotebookSection() {
|
||||||
return (
|
return (
|
||||||
<>
|
<>
|
||||||
<NotebookContainer lightBg={false} id='Notebooks'>
|
<NotebookContainer lightBg={false} id="Notebooks">
|
||||||
<NotebookWrapper>
|
<NotebookWrapper>
|
||||||
<NotebookRow imgStart={true}>
|
<NotebookRow imgStart={true}>
|
||||||
<Column1>
|
<Column1>
|
||||||
<TextWrapper>
|
<TextWrapper>
|
||||||
<TopLine>Coding exercises</TopLine>
|
<TopLine>Coding exercises</TopLine>
|
||||||
<Heading lightText={true}>Python notebooks covering the whole text</Heading>
|
<Heading lightText={true}>
|
||||||
<Subtitle darkText={false}>Sixty eight python notebook exercises with missing code to fill in based on the text</Subtitle>
|
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>
|
</TextWrapper>
|
||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
<ImgWrap>
|
<ImgWrap>
|
||||||
<Img src={img} alt='Car'/>
|
<Img src={img} alt="Coding" />
|
||||||
</ImgWrap>
|
</ImgWrap>
|
||||||
</Column2>
|
</Column2>
|
||||||
</NotebookRow>
|
</NotebookRow>
|
||||||
<NotebookRow>
|
<NotebookRow>
|
||||||
<Column1>
|
<Column1>
|
||||||
<ul>
|
<ul>
|
||||||
<li> Notebook 1.1 - Background mathematics: <NBLink
|
{/* render first half of notebooks*/}
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</NBLink>
|
{notebooks.slice(0, notebooks.length / 2).map((notebook, index) => (
|
||||||
</li>
|
<li key={index}>
|
||||||
<li> Notebook 2.1 - Supervised learning: <NBLink
|
{notebook.text}:{" "}
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</NBLink>
|
<NBLink href={notebook.link}>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>
|
</li>
|
||||||
|
))}
|
||||||
</ul>
|
</ul>
|
||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
<ul>
|
<ul>
|
||||||
<li> Notebook 11.1 - Shattered gradients: <NBLink
|
{/* render second half of notebooks*/}
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </NBLink>
|
{notebooks.slice(notebooks.length / 2).map((notebook, index) => (
|
||||||
|
<li key={index}>
|
||||||
|
{notebook.text}:{" "}
|
||||||
|
<NBLink href={notebook.link}>ipynb/colab</NBLink>
|
||||||
</li>
|
</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>
|
</ul>
|
||||||
</Column2>
|
</Column2>
|
||||||
</NotebookRow>
|
</NotebookRow>
|
||||||
</NotebookWrapper>
|
</NotebookWrapper>
|
||||||
</NotebookContainer>
|
</NotebookContainer>
|
||||||
</>
|
</>
|
||||||
)
|
);
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -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">
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|
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<defs
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id="defs918" />
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|
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<sodipodi:namedview
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id="namedview916"
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|
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pagecolor="#ffffff"
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bordercolor="#666666"
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borderopacity="1.0"
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inkscape:pageshadow="2"
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|
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inkscape:pageopacity="0.0"
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inkscape:pagecheckerboard="0"
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showgrid="false"
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inkscape:zoom="65.6"
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inkscape:cy="4.5426829"
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inkscape:window-width="1296"
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inkscape:window-height="906"
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inkscape:window-x="0"
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inkscape:window-y="38"
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inkscape:window-maximized="0"
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inkscape:current-layer="svg914" />
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<path
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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"
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id="path912"
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||||||
</svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.5 KiB |
@@ -1,5 +1,5 @@
|
|||||||
import React from "react";
|
import React from "react";
|
||||||
import ReactDOM from "react-dom";
|
import ReactDOM from "react-dom/client";
|
||||||
import App from "./App";
|
import App from "./App";
|
||||||
import "./styles/globals.css";
|
import "./styles/globals.css";
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user