From 73833b09445246f557eb37a4b49ce1fb2f1884e0 Mon Sep 17 00:00:00 2001 From: Simon Prince Date: Fri, 5 Apr 2024 17:10:23 -0400 Subject: [PATCH] Updates --- asset-manifest.json | 6 +++--- index.html | 2 +- static/js/main.7569b115.chunk.js | 2 -- static/js/main.7569b115.chunk.js.map | 1 - static/js/main.914954e9.chunk.js | 2 ++ static/js/main.914954e9.chunk.js.map | 1 + 6 files changed, 7 insertions(+), 7 deletions(-) delete mode 100644 static/js/main.7569b115.chunk.js delete mode 100644 static/js/main.7569b115.chunk.js.map create mode 100644 static/js/main.914954e9.chunk.js create mode 100644 static/js/main.914954e9.chunk.js.map diff --git a/asset-manifest.json b/asset-manifest.json index 9e81ec4..b2e4f3a 100644 --- a/asset-manifest.json +++ b/asset-manifest.json @@ -1,8 +1,8 @@ { "files": { "main.css": "/udlbook/static/css/main.e52d581a.chunk.css", - "main.js": "/udlbook/static/js/main.7569b115.chunk.js", - "main.js.map": "/udlbook/static/js/main.7569b115.chunk.js.map", + "main.js": "/udlbook/static/js/main.914954e9.chunk.js", + "main.js.map": "/udlbook/static/js/main.914954e9.chunk.js.map", "runtime-main.js": "/udlbook/static/js/runtime-main.2b958dff.js", "runtime-main.js.map": "/udlbook/static/js/runtime-main.2b958dff.js.map", "static/js/2.e26c66d2.chunk.js": "/udlbook/static/js/2.e26c66d2.chunk.js", @@ -21,6 +21,6 @@ "static/js/runtime-main.2b958dff.js", "static/js/2.e26c66d2.chunk.js", "static/css/main.e52d581a.chunk.css", - "static/js/main.7569b115.chunk.js" + "static/js/main.914954e9.chunk.js" ] } \ No newline at end of file diff --git a/index.html b/index.html index 62279e9..ef63f09 100644 --- a/index.html +++ b/index.html @@ -1 +1 @@ -Understanding Deep Learning
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styled from 'styled-components'\r\nimport {Link as LinkS} from 'react-scroll'\r\nimport {Link as LinkR} from 'react-router-dom'\r\nimport {FaTimes} from 'react-icons/fa'\r\n\r\n\r\n\r\nexport const SidebarContainer = styled.aside`\r\n position:fixed ; \r\n z-index: 999;\r\n width: 100%;\r\n height: 100%;\r\n background: #0d0d0d;\r\n display: grid;\r\n align-items: center;\r\n top: 0;\r\n left: 0;\r\n transition: 0.3s ease-in-out;\r\n opacity: ${({ isOpen }) => (isOpen ? '100%' : '0')};\r\n top: ${({ isOpen }) => (isOpen ? '0' : '-100%')}; \r\n \r\n`\r\n\r\nexport const CloseIcon = styled(FaTimes)`\r\n color: #fff ; \r\n &:hover {\r\n color: #01bf71;\r\n transition: 0.2s ease-in-out;\r\n }\r\n`\r\n\r\nexport const Icon = styled.div`\r\n position: absolute;\r\n top: 1.2rem;\r\n right: 1.5rem;\r\n background: transparent;\r\n font-size: 2rem;\r\n cursor: pointer;\r\n outline: none;\r\n`\r\n\r\nexport const SidebarWrapper = styled.div`\r\n color: #ffffff;\r\n`\r\n\r\nexport const SidebarMenu = styled.ul`\r\n display: grid;\r\n grid-template-columns: 1fr;\r\n grid-template-rows: repeat(6,80px);\r\n text-align: center;\r\n\r\n @media screen and (max-width: 480px){\r\n grid-template-rows: repeat(6, 60px) ; \r\n }\r\n`\r\n\r\nexport const SidebarLink = styled(LinkS)`\r\n display: flex ;\r\n align-items: center;\r\n justify-content: center;\r\n font-size: 1.5rem;\r\n text-decoration: none;\r\n list-style: none;\r\n transition: 0.2s ease-in-out;\r\n text-decoration: none;\r\n color: #fff;\r\n cursor: pointer;\r\n\r\n &:hover {\r\n color: #01bf71;\r\n transition: 0.2s ease-in-out;\r\n }\r\n`\r\n\r\nexport const SideBtnWrap = styled.div` \r\n display: flex;\r\n justify-content: center;\r\n`\r\n\r\nexport const SidebarRoute = styled(LinkR)` \r\n border-radius: 50px;\r\n background: #01bf71;\r\n white-space: nowrap;\r\n padding: 16px 46px;\r\n color: #010606;\r\n font-size: 16px;\r\n outline: none;\r\n border: none;\r\n cursor: pointer;\r\n transition: all 0.2s ease-in-out;\r\n text-decoration: none;\r\n\r\n &:hover {\r\n transition: all 0.2s ease-in-out;\r\n background: #fff;\r\n color: #010606;\r\n }\r\n`","import React from 'react'\r\nimport { SidebarContainer, Icon, CloseIcon, SidebarWrapper, SidebarMenu, SidebarLink} from './SidebarElements'\r\n\r\n\r\nconst Sidebar = ({isOpen, toggle}) => {\r\n return (\r\n <>\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n Notebooks\r\n \r\n \r\n Instructors\r\n \r\n \r\n Media\r\n \r\n \r\n More\r\n \r\n \r\n \r\n \r\n \r\n )\r\n}\r\n\r\nexport default Sidebar\r\n","import { Link as LinkS } from 'react-scroll';\r\nimport { Link as LinkR } from 'react-router-dom';\r\nimport styled from 'styled-components';\r\n\r\nexport const Nav = styled.nav`\r\n background: ${({ scrollNav }) => (scrollNav ? '#000' : 'transparent')};\r\n height: 100px;\r\n margin-top: -100px;\r\n display: flex;\r\n justify-content: center;\r\n align-items: center;\r\n font-size: 1rem;\r\n position: sticky;\r\n top: 0;\r\n z-index: 10;\r\n\r\n @media screen and (max-width: 960px) {\r\n transition: 0.8s all ease;\r\n }\r\n`;\r\n\r\nexport const NavbarContainer = styled.div`\r\n display: flex;\r\n justify-content: space-between;\r\n height: 100px;\r\n z-index: 1;\r\n width: 100%;\r\n padding: 0 24px;\r\n max-width: 1100px;\r\n`;\r\n\r\nexport const NavLogo = styled(LinkR)`\r\n color: #fff;\r\n justify-self: flex-start;\r\n cursor: pointer;\r\n font-size: 1.5rem;\r\n display: flex;\r\n align-items: center;\r\n margin-left: 24px;\r\n font-weight: bold;\r\n text-decoration: none;\r\n`;\r\n\r\nexport const MobileIcon = styled.div`\r\n display: none;\r\n\r\n @media screen and (max-width: 768px) {\r\n display: block;\r\n position: absolute;\r\n top: 0;\r\n right: 0;\r\n transform: translate(-100%, 60%);\r\n font-size: 1.8rem;\r\n cursor: pointer;\r\n }\r\n`;\r\n\r\nexport const NavMenu = styled.ul`\r\n display: flex;\r\n align-items: center;\r\n list-style: none;\r\n text-align: center;\r\n margin-right: -22px;\r\n\r\n @media screen and (max-width: 768px) {\r\n display: none;\r\n }\r\n`;\r\n\r\nexport const NavItem = styled.li`\r\n height: 80px;\r\n`;\r\n\r\nexport const NavBtn = styled.nav`\r\n display: flex;\r\n align-items: center;\r\n\r\n @media screen and (max-width: 768px) {\r\n display: none;\r\n }\r\n`;\r\n\r\nexport const NavLinks = styled(LinkS)`\r\n color: #fff;\r\n display: flex;\r\n align-items: center;\r\n text-decoration: none;\r\n padding: 0 1rem;\r\n height: 100%;\r\n cursor: pointer;\r\n\r\n &.active {\r\n border-bottom: 3px solid #57c6d1\r\n }\r\n`;\r\n\r\nexport const NavBtnLink = styled(LinkR)`\r\n border-radius: 50px;\r\n background: #01bf71;\r\n white-space: nowrap;\r\n padding: 10px 22px;\r\n color: #010606;\r\n font-size: 16px;\r\n outline: none;\r\n border: none;\r\n cursor: pointer;\r\n transition: all 0.2s ease-in-out;\r\n text-decoration: none;\r\n\r\n &:hover {\r\n transition: all 0.2s ease-in-out;\r\n background: #fff;\r\n color: #010606;\r\n }\r\n`;\r\n","import React, {useState, useEffect} from 'react'\r\nimport {FaBars} from 'react-icons/fa'\r\nimport {IconContext} from 'react-icons/lib'\r\nimport {Nav, NavbarContainer, NavLogo, MobileIcon, NavMenu, NavItem, NavLinks} from './NavbarElements'\r\nimport { animateScroll as scroll } from 'react-scroll'\r\n\r\n\r\nconst Navbar = ( {toggle} ) => {\r\n const [scrollNav, setScrollNav] = useState(false)\r\n\r\n const changeNav = () =>{\r\n if (window.scrollY >= 80){\r\n setScrollNav(true)\r\n }else{\r\n setScrollNav(false)\r\n }\r\n }\r\n\r\n useEffect(() =>{\r\n window.addEventListener('scroll', changeNav)\r\n }, [])\r\n\r\n const toggleHome = () => {\r\n scroll.scrollToTop();\r\n }\r\n\r\n return (\r\n <>\r\n \r\n \r\n \r\n \r\n );\r\n}\r\n\r\nexport default Navbar\r\n","import styled from \"styled-components\";\r\n\r\nexport const HeroContainer = styled.div`\r\n background: #57c6d1;\r\n display: flex;\r\n justify-content: center;\r\n align-items: center;\r\n padding: 0 0px;\r\n position: static;\r\n z-index: 1;\r\n }\r\n`\r\n\r\n\r\n\r\nexport const HeroContent = styled.div` \r\n z-index: 3; \r\n width: 100% ; \r\n max-width: 1100px;\r\n position: static;\r\n padding: 8px 24px;\r\n margin: 80px 0px;\r\n display: flex;\r\n flex-direction: column;\r\n align-items: center ; \r\n`\r\nexport const HeroH1 = styled.h1` \r\n color: #fff;\r\n font-size: 48px;\r\n text-align: center;\r\n\r\n @media screen and (max-width: 768px) {\r\n font-size: 40px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 32px;\r\n }\r\n\r\n`\r\n\r\nexport const HeroP = styled.p` \r\n margin-top: 24px;\r\n color: #fff;\r\n font-size: 24px ; \r\n text-align: center ; \r\n max-width: 600px ; \r\n\r\n \r\n @media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n }\r\n`\r\n\r\nexport const HeroBtnWrapper = styled.div`\r\n margin-top: 32px ; \r\n display: flex;\r\n flex-direction: column ; \r\n align-items: center ; \r\n`\r\n\r\n\r\nexport const HeroRow = styled.div` \r\n display: grid; \r\n grid-auto-columns: minmax(auto, 1fr);\r\n align-items: top;\r\n grid-template-areas: 'col1 col2' };\r\n\r\n @media screen and (max-width: 768px){\r\n grid-template-areas: 'col2' 'col1';\r\n }\r\n`\r\n\r\n\r\nexport const HeroNewsItem = styled.div`\r\n margin-left: 4px;\r\n color: #000000;\r\n font-size: 16px;\r\n // line-height: 16px;\r\n margin-bottom: 16px;\r\n display: flex; \r\n justify-content: start;\r\n`\r\nexport const HeroNewsItemDate = styled.div`\r\n width: 20%;\r\n font-size: 16px ; \r\n margin-right: 20px ; \r\n\r\n @media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n }\r\n`\r\n\r\nexport const HeroNewsItemContent = styled.div`\r\n width: 80%; \r\n color: #000000;\r\n font-size: 16px ; \r\n\r\n @media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n }\r\n`\r\n\r\n\r\n\r\nexport const HeroColumn1 = styled.div` \r\n margin-bottom: 15px;\r\n margin-left: 12px;\r\n margin-top: 60px;\r\n padding: 10px 15px;\r\n padding: 0 15px;\r\n grid-area: col1;\r\n align-items:left;\r\n display: flex;\r\n flex-direction:column;\r\n justify-content: space-between;\r\n`\r\n\r\n\r\nexport const HeroColumn2 = styled.div` \r\n margin-bottom: 15px;\r\n padding: 0 15px;\r\n grid-area: col2;\r\n display: flex;\r\n align-items:center;\r\n flex-direction:column;\r\n`\r\n\r\nexport const TextWrapper = styled.div`\r\n max-width: 540px ; \r\n padding-top: 0;\r\n padding-bottom: 0;\r\n`\r\nexport const HeroImgWrap = styled.div` \r\n max-width: 555px;\r\n height: 100%;\r\n`\r\n\r\nexport const Img = styled.img`\r\n width: 100%;\r\n margin-top: 0;\r\n margin-right: 0;\r\n margin-left: 10px;\r\n padding-right: 0;\r\n`;\r\n\r\nexport const HeroDownloadsImg = styled.img`\r\n margin-top: 5px;\r\n margin-right: 0;\r\n margin-left: 0;\r\n padding-right: 0;\r\n margin-bottom: 10px;\r\n`\r\n\r\nexport const HeroLink = styled.a`\r\n color: #fff;\r\n text-decoration: none;\r\n padding: 0.1rem 0rem;\r\n height: 100%;\r\n cursor: pointer;\r\n\r\n &:hover {\r\n filter: brightness(0.85);\r\n }\r\n\r\n &.active {\r\n color: #000\r\n border-bottom: 3px solid #01bf71;\r\n }\r\n`;\r\n\r\n\r\nexport const HeroNewsTitle = styled.div` \r\n margin-left: 0px;\r\n color: #000000;\r\n font-size: 16px;\r\n font-weight: bold;\r\n line-height: 16px;\r\n margin-bottom: 36px; \r\n\r\n @media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n }\r\n`\r\n\r\nexport const HeroCitationTitle = styled.div` \r\n margin-left: 0px;\r\n color: #000000;\r\n font-size: 16px;\r\n font-weight: bold;\r\n line-height: 16px;\r\n margin-bottom: 10px; \r\n margin-top:36px;\r\n\r\n @media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n }\r\n\r\n @media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n }\r\n`\r\n\r\n\r\nexport const HeroNewsBlock = styled.div`\r\n\r\n`\r\nexport const HeroCitationBlock = styled.div`\r\n font-size: 14px;\r\n margin-bottom: 0px;\r\n margin-top: 0px;\r\n\r\n`\r\n\r\n\r\n\r\n\r\nexport const HeroFollowBlock = styled.div`\r\n@media screen and (max-width: 768px) {\r\n font-size: 24px;\r\n}\r\n\r\n@media screen and (max-width: 480px) {\r\n font-size: 18px;\r\n}\r\n`","export default __webpack_public_path__ + \"static/media/F23.prince.learning.turquoise.af513a4e.jpg\";","import React from 'react'\r\nimport { HeroContainer, HeroNewsBlock, HeroCitationBlock, HeroCitationTitle, HeroFollowBlock, HeroDownloadsImg, HeroLink, HeroRow, HeroColumn1, HeroColumn2, HeroContent, Img, HeroImgWrap, HeroNewsTitle, HeroNewsItem, HeroNewsItemDate, HeroNewsItemContent} from './HeroElements'\r\nimport img from '../../images/F23.prince.learning.turquoise.jpg'\r\n\r\nconst HeroSection = () => {\r\n\r\n\r\n const citation = `\r\n@book{prince2023understanding,\r\n author = \"Simon J.D. Prince\",\r\n title = \"Understanding Deep Learning\",\r\n publisher = \"The MIT Press\",\r\n year = 2023,\r\n url = \"http://udlbook.com\"}\r\n `\r\n \r\n return (\r\n \r\n \r\n \r\n \r\n \r\n RECENT NEWS:\r\n\r\n \r\n 03/12/24\r\n Book now available again.\r\n \r\n\r\n\r\n \r\n 02/15/24\r\n First printing of book has sold out in most places. Second printing available mid-March.\r\n \r\n \r\n\r\n \r\n 01/29/24\r\n New blog about gradient flow published.\r\n \r\n\r\n \r\n 12/26/23\r\n Machine Learning Street Talk podcast discussing book.\r\n \r\n\r\n \r\n 12/19/23\r\n Deeper Insights podcast discussing book.\r\n \r\n\r\n \r\n 12/06/23\r\n I did an interview discussing the book with Borealis AI.\r\n \r\n\r\n \r\n 12/05/23\r\n Book released by The MIT Press.\r\n \r\n \r\n \r\n Follow me on Twitter or LinkedIn for updates.\r\n \r\n CITATION:\r\n \r\n 
\r\n                                \r\n                                    {citation}\r\n                                \r\n
\r\n \r\n \r\n \r\n \r\n \"book\r\n \r\n Download full pdf (03 Apr 2024)\r\n \r\n Buy the book\r\n Answers to selected questions\r\n Errata\r\n \r\n \r\n \r\n \r\n )\r\n}\r\n\r\nexport default HeroSection\r\n","import styled from \"styled-components\";\n\n\nexport const NotebookContainer = styled.div` \n color: #fff;\n /* background: #f9f9f9; */\n background: ${({lightBg}) => (lightBg ? '#f9f9f9': '#010606')};\n\n @media screen and (max-width: 768px) {\n padding: 100px 0;\n }\n`\n\nexport const NotebookWrapper = styled.div`\n display: grid ; \n z-index: 1;\n // height: 1250px ; \n width: 100% ; \n max-width: 1100px;\n margin-right: auto;\n margin-left: auto;\n padding: 0 24px;\n justify-content: center;\n`\n\nexport const NotebookRow = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: center;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\nexport const Column1 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col1;\n`\n\nexport const Column2 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col2;\n`\n\nexport const TextWrapper = styled.div`\n max-width: 540px ; \n padding-top: 0;\n padding-bottom: 0;\n`\n\nexport const TopLine = styled.p` \n color: #57c6d1;\n font-size: 16px;\n line-height: 16px;\n font-weight: 700;\n letter-spacing: 1.4px;\n text-transform: uppercase;\n margin-bottom: 16px;\n`\n\nexport const Heading= styled.h1` \n\n margin-bottom: 24px;\n font-size: 48px;\n line-height: 1.1;\n font-weight: 600;\n color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};\n \n @media screen and (max-width: 480px)\n {\n font-size: 32px;\n }\n`\n\n\nexport const Subtitle = styled.p` \n max-width: 440px;\n margin-bottom: 35px;\n font-size: 18px;\n line-height: 24px;\n color: ${({darkText})=> (darkText ? '#010606' : '#fff')};\n\n`\n\nexport const BtnWrap = styled.div`\n display: flex;\n justify-content: flex-start;\n`\n\nexport const ImgWrap = styled.div` \n max-width: 555px;\n height: 100%;\n`\n\nexport const Img = styled.img`\n width: 100%;\n margin-top: 0;\n margin-right: 0;\n margin-left: 10px;\n padding-right: 0;\n`;\n","export default __webpack_public_path__ + \"static/media/coding.e33bff69.svg\";","import React from 'react'\nimport { ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements'\n\n// export const homeObjOne = {\n// id: 'about',\n// lightBg: false,\n// lightText: true,\n// lightTextDesc: true,\n// topLine: 'Premium Bank',\n// headline: 'Unlimited transactions with zero fees',\n// description:\n// 'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees',\n// buttonLabel: 'Get Started',\n// imgStart: false,\n// img: require('../../images/svg-1.svg').default,\n// alt: 'Car',\n// dark: true,\n// primary: true,\n// darkText: false\n// };\n\nimport img from '../../images/coding.svg'\n\n\n\nconst NotebookSection = () => {\n return (\n <>\n \n \n \n \n \n Coding exercises\n Python notebooks covering the whole text\n Sixty eight python notebook exercises with missing code to fill in based on the text\n \n \n \n \n Car\n \n \n \n \n \n
    \n
  • Notebook 1.1 - Background mathematics: ipynb/colab\n
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  • Notebook 2.1 - Supervised learning: ipynb/colab\n
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  • Notebook 3.1 - Shallow networks I: ipynb/colab \n
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  • Notebook 3.2 - Shallow networks II: ipynb/colab \n
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  • Notebook 3.3 - Shallow network regions: ipynb/colab \n
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  • Notebook 3.4 - Activation functions: ipynb/colab \n
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  • Notebook 4.1 - Composing networks: ipynb/colab \n
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  • Notebook 4.2 - Clipping functions: ipynb/colab \n
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  • Notebook 4.3 - Deep networks: ipynb/colab \n
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  • Notebook 5.1 - Least squares loss: ipynb/colab \n
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  • Notebook 5.2 - Binary cross-entropy loss: ipynb/colab \n
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  • Notebook 5.3 - Multiclass cross-entropy loss: ipynb/colab \n
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  • Notebook 6.1 - Line search: ipynb/colab \n
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  • Notebook 6.2 - Gradient descent: ipynb/colab \n
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  • Notebook 6.3 - Stochastic gradient descent: ipynb/colab \n
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  • Notebook 6.4 - Momentum: ipynb/colab \n
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  • Notebook 6.5 - Adam: ipynb/colab \n
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  • Notebook 7.1 - Backpropagation in toy model: ipynb/colab \n
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  • Notebook 7.2 - Backpropagation: ipynb/colab \n
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  • Notebook 7.3 - Initialization: ipynb/colab \n
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  • Notebook 8.1 - MNIST-1D performance: ipynb/colab \n
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  • Notebook 8.2 - Bias-variance trade-off: ipynb/colab \n
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  • Notebook 8.3 - Double descent: ipynb/colab \n
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  • Notebook 8.4 - High-dimensional spaces: ipynb/colab \n
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  • Notebook 9.1 - L2 regularization: ipynb/colab \n
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  • Notebook 9.2 - Implicit regularization: ipynb/colab \n
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  • Notebook 9.3 - Ensembling: ipynb/colab \n
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  • Notebook 9.4 - Bayesian approach: ipynb/colab \n
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  • Notebook 9.5 - Augmentation ipynb/colab \n
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  • Notebook 10.1 - 1D convolution: ipynb/colab \n
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  • Notebook 10.2 - Convolution for MNIST-1D: ipynb/colab \n
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  • Notebook 10.3 - 2D convolution: ipynb/colab \n
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  • Notebook 10.4 - Downsampling & upsampling: ipynb/colab \n
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  • Notebook 10.5 - Convolution for MNIST: ipynb/colab \n
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\n \n \n
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  • Notebook 11.1 - Shattered gradients: ipynb/colab \n
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  • Notebook 11.2 - Residual networks: ipynb/colab \n
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  • Notebook 11.3 - Batch normalization: ipynb/colab \n
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  • Notebook 12.1 - Self-attention: ipynb/colab \n
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  • Notebook 12.2 - Multi-head self-attention: ipynb/colab \n
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  • Notebook 12.3 - Tokenization: ipynb/colab \n
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  • Notebook 12.4 - Decoding strategies: ipynb/colab \n
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  • Notebook 13.1 - Encoding graphs: ipynb/colab \n
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  • Notebook 13.2 - Graph classification : ipynb/colab \n
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  • Notebook 13.3 - Neighborhood sampling: ipynb/colab \n
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  • Notebook 13.4 - Graph attention: ipynb/colab \n
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  • Notebook 15.1 - GAN toy example: ipynb/colab
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  • Notebook 15.2 - Wasserstein distance: ipynb/colab
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  • Notebook 16.1 - 1D normalizing flows: ipynb/colab
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  • Notebook 16.2 - Autoregressive flows: ipynb/colab
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  • Notebook 16.3 - Contraction mappings: ipynb/colab
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  • Notebook 17.1 - Latent variable models: ipynb/colab
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  • Notebook 17.2 - Reparameterization trick: ipynb/colab
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  • Notebook 17.3 - Importance sampling: ipynb/colab
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  • Notebook 18.1 - Diffusion encoder: ipynb/colab
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  • Notebook 18.2 - 1D diffusion model: ipynb/colab
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  • Notebook 18.3 - Reparameterized model: ipynb/colab
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  • Notebook 18.4 - Families of diffusion models: ipynb/colab
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  • Notebook 19.1 - Markov decision processes: ipynb/colab
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  • Notebook 19.2 - Dynamic programming: ipynb/colab
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  • Notebook 19.3 - Monte-Carlo methods: ipynb/colab
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  • Notebook 19.4 - Temporal difference methods: ipynb/colab
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  • Notebook 19.5 - Control variates: ipynb/colab
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  • Notebook 20.1 - Random data: ipynb/colab
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  • Notebook 20.2 - Full-batch gradient descent: ipynb/colab
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  • Notebook 20.3 - Lottery tickets: ipynb/colab
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  • Notebook 20.4 - Adversarial attacks: ipynb/colab
    • \n
  • Notebook 21.1 - Bias mitigation: ipynb/colab
    • \n
  • Notebook 21.2 - Explainability: ipynb/colab
    • \n
\n \n \n\n \n \n \n )\n}\n\nexport default NotebookSection\n","import styled from \"styled-components\";\n\n\nexport const InstructorsContainer = styled.div` \n color: #fff;\n /* background: #f9f9f9; */\n background: ${({lightBg}) => (lightBg ? '#57c6d1': '#010606')};\n\n @media screen and (max-width: 768px) {\n padding: 100px 0;\n }\n`\n\nexport const InstructorsWrapper = styled.div`\n display: grid ; \n z-index: 1;\n width: 100% ; \n max-width: 1100px;\n margin-right: auto;\n margin-left: auto;\n padding: 0 24px;\n justify-content: center;\n`\n\nexport const InstructorsRow = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: center;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\nexport const InstructorsRow2 = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: top;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\n\nexport const Column1 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col1;\n`\n\nexport const Column2 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col2;\n`\n\nexport const TextWrapper = styled.div`\n max-width: 540px ; \n padding-top: 0;\n padding-bottom: 0;\n`\n\nexport const TopLine = styled.p` \n color: #773c23;\n font-size: 16px;\n line-height: 16px;\n font-weight: 700;\n letter-spacing: 1.4px;\n text-transform: uppercase;\n margin-bottom: 16px;\n`\n\nexport const Heading= styled.h1` \n\n margin-bottom: 24px;\n font-size: 48px;\n line-height: 1.1;\n font-weight: 600;\n color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};\n \n @media screen and (max-width: 480px)\n {\n font-size: 32px;\n }\n`\n\n\nexport const Subtitle = styled.p` \n max-width: 440px;\n margin-bottom: 35px;\n font-size: 18px;\n line-height: 24px;\n color: ${({darkText})=> (darkText ? '#010606' : '#fff')};\n\n`\n\nexport const BtnWrap = styled.div`\n display: flex;\n justify-content: flex-start;\n`\n\nexport const ImgWrap = styled.div` \n max-width: 555px;\n height: 100%;\n`\n\nexport const Img = styled.img`\n width: 100%;\n margin-top: 0;\n margin-right: 0;\n margin-left: 10px;\n padding-right: 0;\n`;\n\n\nexport const InstructorsContent = styled.div` \n z-index: 3; \n width: 100% ; \n max-width: 1100px;\n position: static;\n padding: 8px 0px;\n margin: 10px 0px;\n display: flex;\n flex-direction: column;\n align-items: left ; \n list-style-position: inside;\n`","export default __webpack_public_path__ + \"static/media/instructor.956f72a0.svg\";","import React from 'react'\nimport { ImgWrap, Img, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'\n\n// export const homeObjOne = {\n// id: 'about',\n// lightBg: false,\n// lightText: true,\n// lightTextDesc: true,\n// topLine: 'Premium Bank',\n// headline: 'Unlimited transactions with zero fees',\n// description:\n// 'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees',\n// buttonLabel: 'Get Started',\n// imgStart: false,\n// img: require('../../images/svg-1.svg').default,\n// alt: 'Car',\n// dark: true,\n// primary: true,\n// darkText: false\n// };\n\nimport img from '../../images/instructor.svg'\n\n\n\nconst InstructorsSection = () => {\n return (\n <>\n \n \n \n \n \n Instructors\n Resources for instructors\n All the figures in vector and image formats, full slides for first twelve chapters, instructor answer booklet\n \n \n \n \n Car\n \n \n \n \n \n Register\n Register with MIT Press for answer booklet.\n \n\n \n \n Full slides\n \n Slides for 20 lecture undergraduate deep learning course:\n \n \n
    \n
  1. Introduction PPTX
    2. \n
  2. Supervised Learning PPTX
    3. \n
  3. Shallow Neural Networks PPTX
    4. \n
  4. Deep Neural Networks PPTX
    5. \n
  5. Loss Functions PPTX
    6. \n
  6. Fitting Models PPTX
    7. \n
  7. Computing Gradients PPTX
    8. \n
  8. Initialization PPTX
    9. \n
  9. Performance PPTX
    10. \n
  10. Regularization PPTX
    11. \n
  11. Convolutional Networks PPTX
    12. \n
  12. Image Generation PPTX
    13. \n
  13. Transformers and LLMs PPTX
    14. \n
\n \n \n \n Figures\n \n
    \n
  1. Introduction: PDF / SVG / PPTX
    2. \n\n
  2. Supervised learning: PDF / SVG / PPTX
    3. \n
  3. Shallow neural networks: PDF / SVG / PPTX
    4. \n
  4. Deep neural networks: PDF / SVG\n /\n PPTX
    5. \n
  5. Loss functions: PDF\n / \n SVG\n / PPTX
    6. \n
  6. Training models: PDF\n / \n SVG\n / PPTX
    7. \n
  7. Gradients and initialization: PDF / SVG / PPTX
    8. \n
  8. Measuring performance: PDF / SVG / PPTX
    9. \n
  9. Regularization: PDF\n / \n SVG\n / PPTX
    10. \n
  10. Convolutional networks: PDF / SVG / PPTX
    11. \n
  11. Residual networks: PDF / SVG / PPTX
    12. \n
  12. Transformers: PDF / \n SVG / PPTX
    13. \n
  13. Graph neural networks: PDF / SVG / PPTX
    14. \n
  14. Unsupervised learning: PDF / SVG / \n PPTX
    15. \n
  15. GANs: PDF / SVG / PPTX
    16. \n
  16. Normalizing flows: PDF / SVG / PPTX
    17. \n
  17. Variational autoencoders: PDF / SVG / PPTX
    18. \n
  18. Diffusion models: PDF / SVG / \n \n PPTX
    19. \n
  19. Deep reinforcement learning: PDF / SVG / \n PPTX
    20. \n
  20. Why does deep learning work?: PDF / SVG / \n PPTX
    21. \n
  21. Deep learning and ethics: PDF / SVG / PPTX
    22. \n
  22. Appendices - PDF / \n SVG / PPTX
    23. \n
\n \n Instructions for editing equations in figures.\n\n \n \n \n \n \n\n \n \n \n )\n}\n\nexport default InstructorsSection\n","import styled from 'styled-components'\r\nimport {Link} from 'react-router-dom'\r\n\r\nexport const FooterContainer = styled.footer` \r\n background-color: #101522;\r\n`\r\n\r\nexport const FooterWrap = styled.div` \r\n padding: 48x 24px;\r\n display: flex;\r\n flex-direction: column;\r\n justify-content: center;\r\n align-items: center;\r\n max-width: 1100px;\r\n margin: 0 auto;\r\n`\r\n\r\nexport const FooterLinksContainer = styled.div` \r\n display: flex;\r\n justify-content: center;\r\n\r\n @media screen and (max-width: 820px){\r\n padding-top: 32px;\r\n }\r\n`\r\n\r\nexport const FooterLinksWrapper = styled.div` \r\n display: flex;\r\n @media screen and (max-width: 820px){\r\n flex-direction: column;\r\n }\r\n`\r\n\r\nexport const FooterLinkItems = styled.div` \r\n display: flex;\r\n flex-direction: column;\r\n align-items: flex-start;\r\n margin: 16px;\r\n text-align: left;\r\n width: 160px;\r\n box-sizing: border-box;\r\n color: #fff;\r\n\r\n @media screen and (max-width: 420px){\r\n margin: 0;\r\n padding: 10px;\r\n width: 100%;\r\n }\r\n`\r\n\r\nexport const FooterLinkTitle = styled.h1` \r\n font-size: 14px;\r\n margin-bottom: 16px ; \r\n`\r\n\r\nexport const FooterLink = styled(Link)`\r\n color: #ffffff;\r\n text-decoration: none;\r\n margin-bottom: 0.5rem;\r\n font-size: 14px;\r\n\r\n &:hover{\r\n color: #01bf71;\r\n transition: 0.3s ease-in-out;\r\n }\r\n`\r\n\r\nexport const SocialMedia = styled.section` \r\n max-width: 1000px;\r\n width: 100%;\r\n`\r\n\r\nexport const SocialMediaWrap = styled.div` \r\n display: flex;\r\n justify-content: space-between;\r\n align-items: center;\r\n max-width: 1100px; \r\n margin: 20px auto 0 auto ; \r\n\r\n @media screen and (max-width: 820px){\r\n flex-direction: column;\r\n }\r\n`\r\n\r\nexport const SocialAttrWrap = styled.div` \r\n color: #fff;\r\n display: flex;\r\n justify-content: center;\r\n align-items: center;\r\n max-width: 1100px; \r\n margin: 10px auto 0 auto ; \r\n\r\n @media screen and (max-width: 820px){\r\n flex-direction: column;\r\n }\r\n`\r\n\r\nexport const SocialLogo = styled(Link)` \r\n color: #fff;\r\n justify-self: start;\r\n cursor: pointer;\r\n text-decoration: none;\r\n font-size: 1.5rem;\r\n display: flex;\r\n align-items: center;\r\n margin-bottom: 16px;\r\n font-weight: bold;\r\n`\r\n\r\nexport const WebsiteRights = styled.small` \r\n color: #fff ; \r\n margin-bottom: 8px ; \r\n`\r\n\r\nexport const SocialIcons = styled.div`\r\n display: flex;\r\n justify-content: space-between;\r\n align-items: center;\r\n width: 60px;\r\n margin-bottom: 8px ; \r\n`\r\n\r\nexport const SocialIconLink = styled.a` \r\n color: #fff;\r\n font-size: 24px;\r\n`\r\n\r\nexport const FooterImgWrap = styled.div` \r\n max-width: 555px;\r\n height: 100%;\r\n`\r\n\r\nexport const FooterImg = styled.img`\r\n width: 100%;\r\n margin-top: 0;\r\n margin-right: 0;\r\n margin-left: 10px;\r\n padding-right: 0;\r\n`;\r\n","export default __webpack_public_path__ + \"static/media/square-x-twitter.a2a6cc67.svg\";","import React from 'react'\r\nimport { FaLinkedin} from 'react-icons/fa'\r\nimport { FooterContainer, FooterWrap, FooterImg } from './FooterElements'\r\nimport { SocialMedia, SocialMediaWrap, SocialIcons, SocialIconLink, WebsiteRights, SocialLogo } from './FooterElements'\r\nimport { animateScroll as scroll } from 'react-scroll'\r\nimport twitterImg from '../../images/square-x-twitter.svg'\r\n\r\nconst Footer = () => {\r\n const toggleHome = () => {\r\n scroll.scrollToTop();\r\n }\r\n\r\n return (\r\n <>\r\n \r\n \r\n \r\n \r\n \r\n Understanding Deep Learning\r\n \r\n ©{new Date().getFullYear()} Simon J.D. Prince\r\n \r\n Images by StorySet on FreePik: [1] [2] [3] [4] \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n )\r\n}\r\n\r\nexport default Footer\r\n","import styled from \"styled-components\";\n\n\nexport const MediaContainer = styled.div` \n color: #fff;\n /* background: #f9f9f9; */\n background: ${({lightBg}) => (lightBg ? '#f9f9f9': '#010606')};\n\n @media screen and (max-width: 768px) {\n padding: 100px 0;\n }\n`\n\nexport const MediaWrapper = styled.div`\n display: grid ; \n z-index: 1;\n width: 100% ; \n max-width: 1100px;\n margin-right: auto;\n margin-left: auto;\n padding: 0 24px;\n justify-content: center;\n`\n\nexport const MediaRow = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: center;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\nexport const Column1 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col1;\n`\n\nexport const Column2 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col2;\n`\n\nexport const TextWrapper = styled.div`\n max-width: 540px ; \n padding-top: 0;\n padding-bottom: 0;\n`\n\nexport const TopLine = styled.p` \n color: #57c6d1;\n font-size: 16px;\n line-height: 16px;\n font-weight: 700;\n letter-spacing: 1.4px;\n text-transform: uppercase;\n margin-bottom: 16px;\n`\n\nexport const Heading= styled.h1` \n\n margin-bottom: 24px;\n font-size: 48px;\n line-height: 1.1;\n font-weight: 600;\n color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};\n \n @media screen and (max-width: 480px)\n {\n font-size: 32px;\n }\n`\n\n\nexport const Subtitle = styled.p` \n max-width: 440px;\n margin-bottom: 35px;\n font-size: 18px;\n line-height: 24px;\n color: ${({darkText})=> (darkText ? '#010606' : '#fff')};\n\n`\n\nexport const BtnWrap = styled.div`\n display: flex;\n justify-content: flex-start;\n`\n\nexport const ImgWrap = styled.div` \n max-width: 555px;\n height: 100%;\n`\n\nexport const Img = styled.img`\n width: 100%;\n margin-top: 0;\n margin-right: 0;\n margin-left: 10px;\n padding-right: 0;\n`;\n\n\nexport const MediaTextBlock = styled.div`\n@media screen and (max-width: 768px) {\n font-size: 24px;\n}\n\n@media screen and (max-width: 480px) {\n font-size: 18px;\n}\n`\n\nexport const MediaContent = styled.div` \n z-index: 3; \n width: 100% ; \n max-width: 1100px;\n position: static;\n padding: 8px 0px;\n margin: 10px 0px;\n display: flex;\n flex-direction: column;\n align-items: left ; \n list-style-position: inside;\n`\n\nexport const MediaRow2 = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: top;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`","export default __webpack_public_path__ + \"static/media/media.9f345230.svg\";","import React from 'react'\nimport { ImgWrap, Img, MediaContainer, MediaContent, MediaWrapper, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'\n\n// export const homeObjOne = {\n// id: 'about',\n// lightBg: false,\n// lightText: true,\n// lightTextDesc: true,\n// topLine: 'Premium Bank',\n// headline: 'Unlimited transactions with zero fees',\n// description:\n// 'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees',\n// buttonLabel: 'Get Started',\n// imgStart: false,\n// img: require('../../images/svg-1.svg').default,\n// alt: 'Car',\n// dark: true,\n// primary: true,\n// darkText: false\n// };\n\nimport img from '../../images/media.svg'\n\n\n\nconst MediaSection = () => {\n return (\n <>\n \n \n \n \n \n Media\n Reviews, videos, podcasts, interviews\n Various resources connected to the book \n \n \n \n \n Car\n \n \n \n \n \n Machine learning street talk podcast\n \n \n \n Deeper insights podcast \n \n \n \n \n \n Reviews\n \n \n \n \n \n Interviews\n \n \n \n \n \n\n \n \n \n )\n}\n\nexport default MediaSection\n","import styled from \"styled-components\";\n\n\nexport const MoreContainer = styled.div` \n color: #fff;\n /* background: #f9f9f9; */\n background: ${({lightBg}) => (lightBg ? '#57c6d1': '#010606')};\n\n @media screen and (max-width: 768px) {\n padding: 100px 0;\n }\n`\n\nexport const MoreWrapper = styled.div`\n display: grid ; \n z-index: 1;\n // height: 1050px ; \n width: 100% ; \n max-width: 1100px;\n margin-right: auto;\n margin-left: auto;\n padding: 0 24px;\n justify-content: center;\n`\n\nexport const MoreRow = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: center;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\nexport const MoreRow2 = styled.div` \n display: grid; \n grid-auto-columns: minmax(auto, 1fr);\n align-items: top;\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};\n\n @media screen and (max-width: 768px){\n grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};\n }\n`\n\n\nexport const Column1 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col1;\n`\n\nexport const Column2 = styled.div` \n margin-bottom: 15px;\n padding: 0 15px;\n grid-area: col2;\n`\n\nexport const TextWrapper = styled.div`\n max-width: 540px ; \n padding-top: 0;\n padding-bottom: 0;\n`\n\nexport const TopLine = styled.p` \n color: #773c23;\n font-size: 16px;\n line-height: 16px;\n font-weight: 700;\n letter-spacing: 1.4px;\n text-transform: uppercase;\n margin-bottom: 12px;\n margin-top: 16px ; \n`\n\nexport const Heading= styled.h1` \n\n margin-bottom: 24px;\n font-size: 48px;\n line-height: 1.1;\n font-weight: 600;\n color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};\n \n @media screen and (max-width: 480px)\n {\n font-size: 32px;\n }\n`\n\n\nexport const Subtitle = styled.p` \n max-width: 440px;\n margin-bottom: 35px;\n font-size: 18px;\n line-height: 24px;\n color: ${({darkText})=> (darkText ? '#010606' : '#fff')};\n\n`\n\nexport const BtnWrap = styled.div`\n display: flex;\n justify-content: flex-start;\n`\n\nexport const ImgWrap = styled.div` \n max-width: 555px;\n height: 100%;\n`\n\nexport const Img = styled.img`\n width: 100%;\n margin-top: 0;\n margin-right: 0;\n margin-left: 10px;\n padding-right: 0;\n`;\n\n\nexport const MoreContent = styled.div` \n z-index: 3; \n width: 100% ; \n max-width: 1100px;\n position: static;\n padding: 8px 0px;\n margin: 10px 0px;\n display: flex;\n flex-direction: column;\n align-items: left ; \n list-style-position: inside;\n`\n\nexport const MoreOuterList = styled.ul`\n // list-style:none;\n list-style-position: inside;\n margin:0;\n`\n\nexport const MoreInnerList = styled.ul`\n list-style-position: inside;\n`\n\nexport const MoreInnerP = styled.p`\n padding-left: 18px;\n padding-bottom: 10px ; \n padding-top: 3px ; \n font-size:14px;\n color: #fff\n`\n\nexport const MoreLink = styled.a`\n color: #fff;\n text-decoration: none;\n padding: 0.1rem 0rem;\n height: 100%;\n cursor: pointer;\n\n &:hover {\n filter: brightness(0.85);\n }\n\n &.active {\n color: #000\n border-bottom: 3px solid #01bf71;\n }\n`;\n","export default __webpack_public_path__ + \"static/media/more.306a6229.svg\";","import React from 'react'\nimport { ImgWrap, Img, MoreContainer, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'\nimport img from '../../images/more.svg'\n\n\nconst MoreSection = () => {\n return (\n <>\n \n \n \n \n \n More\n Further reading\n Other articles, blogs, and books that I have written. Most in a similar style and using the same notation as Understanding Deep Learning. \n \n \n \n \n Car\n \n \n \n \n \n \n Book\n \n
  • \n Computer vision: models, learning, and inference\n \n \n
  • 2012 book published with CUP
    • \n
  • Focused on probabilistic models
    • \n
  • Pre-\"deep learning\"
    • \n
  • Lots of ML content
    • \n
  • Individual chapters available below
    • \n \n \n \n     \n\n Transformers & LLMs\n \n
  • \n Intro to LLMs\n \n \n
  • What is an LLM?
    • \n
  • Pretraining
    • \n
  • Instruction fine-tuning
    • \n
  • Reinforcement learning from human feedback
    • \n
  • Notable LLMs
    • \n
  • LLMs without training from scratch
    • \n \n \n \n
  • \n Transformers I\n \n \n
  • Dot-Product self-attention
    • \n
  • Scaled dot-product self-attention
    • \n
  • Position encoding
    • \n
  • Multiple heads
    • \n
  • Transformer block
    • \n
  • Encoders
    • \n
  • Decoders
    • \n
  • Encoder-Decoders
    • \n \n \n \n
  • \n Transformers II\n \n \n
  • Sinusoidal position embeddings
    • \n
  • Learned position embeddings
    • \n
  • Relatives vs. absolute position embeddings
    • \n
  • Extending transformers to longer sequences
    • \n
  • Reducing attention matrix size
    • \n
  • Making attention matrix sparse
    • \n
  • Kernelizing attention computation
    • \n
  • Attention as an RNN
    • \n
  • Attention as a hypernetwork
    • \n
  • Attention as a routing network
    • \n
  • Attention and graphs
    • \n
  • Attention and convolutions
    • \n
  • Attention and gating
    • \n
  • Attention and memory retrieval
    • \n \n \n \n
  • \n Transformers III\n \n \n
  • Tricks for training transformers
    • \n
  • Why are these tricks required?
    • \n
  • Removing layer normalization
    • \n
  • Balancing residual dependencies
    • \n
  • Reducing optimizer variance
    • \n
  • How to train deeper transformers on small datasets
    • \n \n \n \n
  • \n Training and fine-tuning LLMs\n \n \n
  • Large language models
    • \n
  • Pretraining
    • \n
  • Supervised fine tuning
    • \n
  • Reinforcement learning from human feedback
    • \n
  • Direct preference optimization
    • \n \n \n \n
  • \n Speeding up inference in LLMs\n \n \n
  • Problems with transformers
    • \n
  • Attention-free transformers
    • \n
  • Complexity
    • \n
  • RWKV
    • \n
  • Linear transformers and performers
    • \n
  • Retentive network
    • \n \n \n \n     \n\n Math for machine learning\n \n
  • \n Linear algebra\n \n \n
  • Vectors and matrices
    • \n
  • Determinant and trace
    • \n
  • Orthogonal matrices
    • \n
  • Null space
    • \n
  • Linear transformations
    • \n
  • Singular value decomposition
    • \n
  • Least squares problems
    • \n
  • Principal direction problems
    • \n
  • Inversion of block matrices
    • \n
  • Schur complement identity
    • \n
  • Sherman-Morrison-Woodbury
    • \n
  • Matrix determinant lemma
    • \n \n \n \n
  • \n Introduction to probability\n \n \n
  • Random variables
    • \n
  • Joint probability
    • \n
  • Marginal probability
    • \n
  • Conditional probability
    • \n
  • Bayes' rule
    • \n
  • Independence
    • \n
  • Expectation
    • \n \n \n \n
  • \n Probability distributions\n \n \n
  • Bernouilli distribution
    • \n
  • Beta distribution
    • \n
  • Categorical distribution
    • \n
  • Dirichlet distribution
    • \n
  • Univariate normal distribution
    • \n
  • Normal inverse-scaled gamma distribution
    • \n
  • Multivariate normal distribution
    • \n
  • Normal inverse Wishart distribution
    • \n
  • Conjugacy
    • \n \n \n \n
  • \n Fitting probability distributions\n \n \n
  • Maximum likelihood
    • \n
  • Maximum a posteriori
    • \n
  • Bayesian approach
    • \n
  • Example: fitting normal
    • \n
  • Example: fitting categorical
    • \n \n \n \n
  • \n The normal distribution\n \n \n
  • Types of covariance matrix
    • \n
  • Decomposition of covariance
    • \n
  • Linear transformations
    • \n
  • Marginal distributions
    • \n
  • Conditional distributions
    • \n
  • Product of two normals
    • \n
  • Change of variable formula
    • \n \n \n \n     \n\n Optimization\n \n
  • \n Gradient-based optimmization\n \n \n
  • Convexity
    • \n
  • Steepest descent
    • \n
  • Newton's method
    • \n
  • Gauss-Newton method
    • \n
  • Line search
    • \n
  • Reparameterization
    • \n \n \n \n
  • \n Bayesian optimization\n \n \n
  • Gaussian processes
    • \n
  • Acquisition functions
    • \n
  • Incorporating noise
    • \n
  • Kernel choice
    • \n
  • Learning GP parameters
    • \n
  • Tips, tricks, and limitations
    • \n
  • Beta-Bernoulli bandit
    • \n
  • Random forests for BO
    • \n
  • Tree-Parzen estimators
    • \n \n \n \n
  • \n SAT Solvers I\n \n \n
  • Boolean logic and satisfiability
    • \n
  • Conjunctive normal form
    • \n
  • The Tseitin transformation
    • \n
  • SAT and related problems
    • \n
  • SAT constructions
    • \n
  • Graph coloring and scheduling
    • \n
  • Fitting binary neural networks
    • \n
  • Fitting decision trees
    • \n \n \n \n
  • \n SAT Solvers II\n \n \n
  • Conditioning
    • \n
  • Resolution
    • \n
  • Solving 2-SAT by unit propagation
    • \n
  • Directional resolution
    • \n
  • SAT as binary search
    • \n
  • DPLL
    • \n
  • Conflict driven clause learning
    • \n \n \n \n
  • \n SAT Solvers III\n \n \n
  • Satisfiability vs. problem size
    • \n
  • Factor graph representation
    • \n
  • Max product / sum product for SAT
    • \n
  • Survey propagation
    • \n
  • SAT with non-binary variables
    • \n
  • SMT solvers
    • \n \n \n \n     \n
  • \n SAT Solvers III\n \n \n
  • Satisfiability vs. problem size
    • \n
  • Factor graph representation
    • \n
  • Max product / sum product for SAT
    • \n
  • Survey propagation
    • \n
  • SAT with non-binary variables
    • \n
  • SMT solvers
    • \n \n \n \n\n Computer vision\n \n
  • \n Image Processing\n \n \n
  • Whitening
    • \n
  • Histogram equalization
    • \n
  • Filtering
    • \n
  • Edges and corners
    • \n
  • Dimensionality reduction
    • \n \n \n \n
  • \n Pinhole camera\n \n \n
  • Pinhole camera model
    • \n
  • Radial distortion
    • \n
  • Homogeneous coordinates
    • \n
  • Learning extrinsic parameters
    • \n
  • Learning intrinsic parameters
    • \n
  • Inferring three-dimensional world points
    • \n \n \n \n
  • \n Geometric transformations\n \n \n
  • Euclidean, similarity, affine, projective transformations
    • \n
  • Fitting transformation models
    • \n
  • Inference in transformation models
    • \n
  • Three geometric problems for planes
    • \n
  • Transformations between images
    • \n
  • Robust learning of transformations
    • \n \n \n \n
  • \n Multiple cameras\n \n \n
  • Two view geometry
    • \n
  • The essential matrix
    • \n
  • The fundamental matrix
    • \n
  • Two-view reconstruction pipeline
    • \n
  • Rectification
    • \n
  • Multiview reconstruction
    • \n \n \n \n     \n\n Reinforcement learning\n \n
  • \n Transformers in RL\n \n \n
  • Challenges in RL
    • \n
  • Advantages of transformers for RL
    • \n
  • Representation learning
    • \n
  • Transition function learning
    • \n
  • Reward learning
    • \n
  • Policy learning
    • \n
  • Training strategy
    • \n
  • Interpretability
    • \n
  • Applications
    • \n \n \n \n     \n     \n\n {/* ########################################### */}\n\n \n AI Theory\n \n
  • \n Gradient flow\n \n \n
  • Gradient flow
    • \n
  • Evolution of residual
    • \n
  • Evolution of parameters
    • \n
  • Evolution of model predictions
    • \n
  • Evolution of prediction covariance
    • \n \n \n \n
  • \n Neural tangent kernel\n \n \n
  • Infinite width neural networks
    • \n
  • Training dynamics
    • \n
  • Empirical NTK for shallow network
    • \n
  • Analytical NTK for shallow network
    • \n
  • Empirical NTK for ddep network
    • \n
  • Analtical NTK for deep network
    • \n \n \n \n     \n\n Temporal models\n \n
  • \n Temporal models\n \n \n
  • Kalman filter
    • \n
  • Smoothing
    • \n
  • Extended Kalman filter
    • \n
  • Unscented Kalman filter
    • \n
  • Particle filtering
    • \n \n \n \n     \n\n Unsupervised learning\n \n
  • \n Modeling complex data densities\n \n \n
  • Hidden variables
    • \n
  • Expectation maximization
    • \n
  • Mixture of Gaussians
    • \n
  • The t-distribution
    • \n
  • Factor analysis
    • \n
  • The EM algorithm in detail
    • \n \n \n \n\n
  • \n Variational autoencoders\n \n \n
  • Non-linear latent variable models
    • \n
  • Evidence lower bound (ELBO)
    • \n
  • ELBO properties
    • \n
  • Variational approximation
    • \n
  • The variational autoencoder
    • \n
  • Reparameterization trick
    • \n \n \n \n
  • \n Normalizing flows: introduction and review\n \n \n
  • Normalizing flows
    • \n
  • Elementwise and linear flows
    • \n
  • Planar and radial flows
    • \n
  • Coupling and auto-regressive flows
    • \n
  • Coupling functions
    • \n
  • Residual flows
    • \n
  • Infinitesimal (continuous) flows
    • \n
  • Datasets and performance
    • \n \n \n \n     \n Graphical Models\n \n
  • \n Graphical models\n \n \n
  • Conditional independence
    • \n
  • Directed graphical models
    • \n
  • Undirected graphical models
    • \n
  • Inference in graphical models
    • \n
  • Sampling in graphical models
    • \n
  • Learning in graphical models
    • \n \n \n \n
  • \n Models for chains and trees\n \n \n
  • Hidden Markov models
    • \n
  • Viterbi algorithm
    • \n
  • Forward-backward algorithm
    • \n
  • Belief propagation
    • \n
  • Sum product algorithm
    • \n
  • Extension to trees
    • \n
  • Graphs with loops
    • \n \n \n \n
  • \n Models for grids\n \n \n
  • Markov random fields
    • \n
  • MAP inference in binary pairwise MRFs
    • \n
  • Graph cuts
    • \n
  • Multi-label pairwise MRFs
    • \n
  • Alpha-expansion algorithm
    • \n
  • Conditional random fields
    • \n \n \n \n     \n\n Machine learning\n \n
  • \n Learning and inference\n \n \n
  • Discriminative models
    • \n
  • Generative models
    • \n
  • Example: regression
    • \n
  • Example: classification
    • \n \n \n \n
  • \n Regression models\n \n \n
  • Linear regression
    • \n
  • Bayesian linear regression
    • \n
  • Non-linear regression
    • \n
  • Bayesian non-linear regression
    • \n
  • The kernel trick
    • \n
  • Gaussian process regression
    • \n
  • Sparse linear regression
    • \n
  • Relevance vector regression
    • \n \n \n \n
  • \n Classification models\n \n \n
  • Logistic regression
    • \n
  • Bayesian logistic regression
    • \n
  • Non-linear logistic regression
    • \n
  • Gaussian process classification
    • \n
  • Relevance vector classification
    • \n
  • Incremental fitting: boosting and trees
    • \n
  • Multi-class logistic regression
    • \n \n \n \n
  • \n Few-shot learning and meta-learning I\n \n \n
  • Meta-learning framework
    • \n
  • Approaches to meta-learning
    • \n
  • Matching networks
    • \n
  • Prototypical networks
    • \n
  • Relation networks
    • \n \n \n \n
  • \n Few-shot learning and meta-learning II\n \n \n
  • MAML & Reptile
    • \n
  • LSTM based meta-learning
    • \n
  • Reinforcement learning based approaches
    • \n
  • Memory augmented neural networks
    • \n
  • SNAIL
    • \n
  • Generative models
    • \n
  • Data augmentation approaches
    • \n \n \n \n     \n\n Natural language processing\n \n
  • \n Neural natural language generation I\n \n \n
  • Encoder-decoder architecture
    • \n
  • Maximum-likelihood training
    • \n
  • Greedy search
    • \n
  • Beam search
    • \n
  • Diverse beam search
    • \n
  • Top-k sampling
    • \n
  • Nucleus sampling
    • \n \n \n \n
  • \n Neural natural language generation II\n \n \n
  • Fine-tuning with reinforcement learning
    • \n
  • Training from scratch with RL
    • \n
  • RL vs. structured prediction
    • \n
  • Minimum risk training
    • \n
  • Scheduled sampling
    • \n
  • Beam search optimization
    • \n
  • SeaRNN
    • \n
  • Reward-augmented maximum likelihood
    • \n \n \n \n
  • \n Parsing I\n \n \n
  • Parse trees
    • \n
  • Context-free grammars
    • \n
  • Chomsky normal form
    • \n
  • CYK recognition algorithm
    • \n
  • Worked example
    • \n \n \n \n
  • \n Parsing II\n \n \n
  • Weighted context-free grammars
    • \n
  • Semirings
    • \n
  • Inside algorithm
    • \n
  • Inside weights
    • \n
  • Weighted parsing
    • \n \n \n \n
  • \n Parsing III\n \n \n
  • Probabilistic context-free grammars
    • \n
  • Parameter estimation (supervised)
    • \n
  • Parameter estimation (unsupervised)
    • \n
  • Viterbi training
    • \n
  • Expectation maximization
    • \n
  • Outside from inside
    • \n
  • Interpretation of outside weights
    • \n \n \n \n
  • \n XLNet\n \n \n
  • Language modeling
    • \n
  • XLNet training objective
    • \n
  • Permutations
    • \n
  • Attention mask
    • \n
  • Two stream self-attention
    • \n \n \n \n     \n\n \n \n Responsible AI\n \n
  • \n Bias and fairness\n \n \n
  • Sources of bias
    • \n
  • Demographic Parity
    • \n
  • Equality of odds
    • \n
  • Equality of opportunity
    • \n
  • Individual fairness
    • \n
  • Bias mitigation
    • \n \n \n \n
  • \n Explainability I\n \n \n
  • Taxonomy of XAI approaches
    • \n
  • Local post-hoc explanations
    • \n
  • Individual conditional explanation
    • \n
  • Counterfactual explanations
    • \n
  • LIME & Anchors
    • \n
  • Shapley additive explanations & SHAP
    • \n \n \n \n
  • \n Explainability II\n \n \n
  • Global feature importance
    • \n
  • Partial dependence & ICE plots
    • \n
  • Accumulated local effects
    • \n
  • Aggregate SHAP values
    • \n
  • Prototypes & criticisms
    • \n
  • Surrogate / proxy models
    • \n
  • Inherently interpretable models
    • \n \n \n \n
  • \n Differential privacy I\n \n \n
  • Early approaches to privacy
    • \n
  • Fundamental law of information recovery
    • \n
  • Differential privacy
    • \n
  • Properties of differential privacy
    • \n
  • The Laplace mechanism
    • \n
  • Examples
    • \n
  • Other mechanisms and definitions
    • \n \n \n \n
  • \n Differential privacy II\n \n \n
  • Differential privacy and matchine learning
    • \n
  • DPSGD
    • \n
  • PATE
    • \n
  • Differentially private data generation
    • \n
  • DPGAN
    • \n
  • PateGAN
    • \n \n \n \n     \n     \n     \n     \n     \n \n )\n}\n\nexport default MoreSection\n\n\n","import React, {useState} from 'react'\r\nimport Sidebar from '../components/Sidebar'\r\nimport Navbar from '../components/NavBar'\r\nimport HeroSection from '../components/HeroSection';\r\nimport NotebookSection from '../components/Notebooks'\r\nimport InstructorsSection from '../components/Instructors';\r\nimport Footer from '../components/Footer';\r\nimport MediaSection from '../components/Media';\r\nimport MoreSection from '../components/More';\r\n\r\nconst Home = () => {\r\n const [isOpen, setIsOpen] = useState(false)\r\n\r\n const toggle = () => {\r\n setIsOpen(!isOpen)\r\n };\r\n\r\n return ( \r\n <>\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n