setup gh-pages
This commit is contained in:
Simon Prince
167
src/components/More/MoreElements.js
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167
src/components/More/MoreElements.js
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import styled from "styled-components";
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export const MoreContainer = styled.div`
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color: #fff;
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/* background: #f9f9f9; */
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background: ${({lightBg}) => (lightBg ? '#57c6d1': '#010606')};
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@media screen and (max-width: 768px) {
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padding: 100px 0;
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}
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`
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export const MoreWrapper = styled.div`
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display: grid ;
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z-index: 1;
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// height: 1050px ;
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width: 100% ;
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max-width: 1100px;
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margin-right: auto;
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margin-left: auto;
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padding: 0 24px;
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justify-content: center;
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`
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export const MoreRow = styled.div`
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display: grid;
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grid-auto-columns: minmax(auto, 1fr);
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align-items: center;
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grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
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@media screen and (max-width: 768px){
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grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
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}
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`
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export const MoreRow2 = styled.div`
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display: grid;
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grid-auto-columns: minmax(auto, 1fr);
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align-items: top;
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grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
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@media screen and (max-width: 768px){
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grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
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}
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`
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export const Column1 = styled.div`
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margin-bottom: 15px;
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padding: 0 15px;
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grid-area: col1;
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`
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export const Column2 = styled.div`
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margin-bottom: 15px;
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padding: 0 15px;
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grid-area: col2;
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`
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export const TextWrapper = styled.div`
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max-width: 540px ;
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padding-top: 0;
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padding-bottom: 0;
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`
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export const TopLine = styled.p`
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color: #773c23;
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font-size: 16px;
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line-height: 16px;
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font-weight: 700;
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letter-spacing: 1.4px;
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text-transform: uppercase;
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margin-bottom: 12px;
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margin-top: 16px ;
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`
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export const Heading= styled.h1`
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margin-bottom: 24px;
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font-size: 48px;
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line-height: 1.1;
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font-weight: 600;
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color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
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@media screen and (max-width: 480px)
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{
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font-size: 32px;
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}
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`
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export const Subtitle = styled.p`
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max-width: 440px;
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margin-bottom: 35px;
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font-size: 18px;
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line-height: 24px;
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color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
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`
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export const BtnWrap = styled.div`
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display: flex;
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justify-content: flex-start;
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`
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export const ImgWrap = styled.div`
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max-width: 555px;
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height: 100%;
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`
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export const Img = styled.img`
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width: 100%;
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margin-top: 0;
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margin-right: 0;
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margin-left: 10px;
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padding-right: 0;
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`;
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export const MoreContent = styled.div`
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z-index: 3;
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width: 100% ;
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max-width: 1100px;
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position: static;
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padding: 8px 0px;
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margin: 10px 0px;
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display: flex;
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flex-direction: column;
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align-items: left ;
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list-style-position: inside;
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`
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export const MoreOuterList = styled.ul`
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// list-style:none;
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list-style-position: inside;
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margin:0;
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`
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export const MoreInnerList = styled.ul`
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list-style-position: inside;
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`
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export const MoreInnerP = styled.p`
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padding-left: 18px;
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padding-bottom: 10px ;
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padding-top: 3px ;
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font-size:14px;
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color: #fff
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`
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export const MoreLink = styled.a`
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color: #fff;
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text-decoration: none;
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padding: 0.1rem 0rem;
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height: 100%;
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cursor: pointer;
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&:hover {
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filter: brightness(0.85);
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}
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&.active {
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color: #000
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border-bottom: 3px solid #01bf71;
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}
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`;
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750
src/components/More/index.js
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750
src/components/More/index.js
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import React from 'react'
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import { ImgWrap, Img, MoreContainer, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
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import img from '../../images/more.svg'
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const MoreSection = () => {
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return (
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<>
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<MoreContainer lightBg={true} id='More'>
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<MoreWrapper>
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<MoreRow imgStart={false}>
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<Column1>
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<TextWrapper>
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<TopLine>More</TopLine>
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<Heading lightText={false}>Further reading</Heading>
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<Subtitle darkText={true}>Other articles, blogs, and books that I have written. Most in a similar style and using the same notation as Understanding Deep Learning. </Subtitle>
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</TextWrapper>
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</Column1>
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<Column2>
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<ImgWrap>
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<Img src={img} alt='Car'/>
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</ImgWrap>
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</Column2>
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</MoreRow>
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<MoreRow2>
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<Column1>
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<TopLine>Book</TopLine>
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<MoreOuterList>
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<li>
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<a href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> 2012 book published with CUP </li>
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<li> Focused on probabilistic models </li>
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<li> Pre-"deep learning" </li>
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<li> Lots of ML content</li>
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<li> Individual chapters available below</li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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</MoreOuterList>
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<TopLine>Transformers & LLMs</TopLine>
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<MoreOuterList>
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<li>
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<a href="https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/" target="_blank" rel="noreferrer">Intro to LLMs</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> What is an LLM?</li>
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<li> Pretraining</li>
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<li> Instruction fine-tuning</li>
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<li> Reinforcement learning from human feedback</li>
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<li> Notable LLMs</li>
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<li> LLMs without training from scratch</li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank" rel="noreferrer">Transformers I</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Dot-Product self-attention </li>
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<li> Scaled dot-product self-attention </li>
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<li> Position encoding</li>
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<li> Multiple heads </li>
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<li> Transformer block </li>
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<li> Encoders </li>
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<li> Decoders </li>
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<li> Encoder-Decoders </li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank" rel="noreferrer">Transformers II</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Sinusoidal position embeddings </li>
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<li> Learned position embeddings </li>
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<li> Relatives vs. absolute position embeddings</li>
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<li> Extending transformers to longer sequences </li>
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<li> Reducing attention matrix size </li>
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<li> Making attention matrix sparse </li>
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<li> Kernelizing attention computation </li>
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<li> Attention as an RNN</li>
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<li> Attention as a hypernetwork </li>
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<li> Attention as a routing network </li>
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<li> Attention and graphs </li>
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<li> Attention and convolutions </li>
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<li> Attention and gating </li>
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<li> Attention and memory retrieval </li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank" rel="noreferrer">Transformers III</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Tricks for training transformers </li>
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<li> Why are these tricks required? </li>
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<li> Removing layer normalization</li>
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<li> Balancing residual dependencies </li>
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<li> Reducing optimizer variance </li>
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<li> How to train deeper transformers on small datasets </li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/" target="_blank" rel="noreferrer">Training and fine-tuning LLMs</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Large language models </li>
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<li> Pretraining </li>
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<li> Supervised fine tuning</li>
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<li> Reinforcement learning from human feedback </li>
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<li> Direct preference optimization</li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/" target="_blank" rel="noreferrer">Speeding up inference in LLMs</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Problems with transformers</li>
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<li> Attention-free transformers </li>
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<li> Complexity</li>
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<li> RWKV </li>
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<li> Linear transformers and performers</li>
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<li> Retentive network</li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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</MoreOuterList>
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<TopLine>Math for machine learning</TopLine>
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<MoreOuterList>
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<li>
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<a href="https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing" target="_blank" rel="noreferrer">Linear algebra</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Vectors and matrices </li>
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<li> Determinant and trace </li>
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<li> Orthogonal matrices </li>
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<li> Null space </li>
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<li> Linear transformations </li>
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<li> Singular value decomposition </li>
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<li> Least squares problems </li>
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<li> Principal direction problems </li>
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<li> Inversion of block matrices</li>
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<li> Schur complement identity</li>
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<li> Sherman-Morrison-Woodbury</li>
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<li> Matrix determinant lemma</li>
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</MoreInnerList>
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</MoreInnerP>
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</li>
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<li>
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<a href="https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing" target="_blank" rel="noreferrer">Introduction to probability</a>
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<MoreInnerP>
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<MoreInnerList>
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<li> Random variables </li>
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<li> Joint probability </li>
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<li> Marginal probability </li>
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<li> Conditional probability </li>
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<li> Bayes' rule </li>
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<li> Independence </li>
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<li> Expectation </li>
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</MoreInnerList>
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</MoreInnerP>
|
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</li>
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||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank" rel="noreferrer">Probability distributions</a>
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<MoreInnerP>
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||||
<MoreInnerList>
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<li> Bernouilli distribution </li>
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<li> Beta distribution</li>
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<li> Categorical distribution </li>
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<li> Dirichlet distribution</li>
|
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<li> Univariate normal distribution </li>
|
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<li> Normal inverse-scaled gamma distribution </li>
|
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<li> Multivariate normal distribution </li>
|
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<li> Normal inverse Wishart distribution </li>
|
||||
<li> Conjugacy </li>
|
||||
</MoreInnerList>
|
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</MoreInnerP>
|
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</li>
|
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<li>
|
||||
<a href="https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing" target="_blank" rel="noreferrer">Fitting probability distributions</a>
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<MoreInnerP>
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||||
<MoreInnerList>
|
||||
<li> Maximum likelihood </li>
|
||||
<li> Maximum a posteriori </li>
|
||||
<li> Bayesian approach </li>
|
||||
<li> Example: fitting normal </li>
|
||||
<li> Example: fitting categorical </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank" rel="noreferrer">The normal distribution</a>
|
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<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Types of covariance matrix </li>
|
||||
<li> Decomposition of covariance </li>
|
||||
<li> Linear transformations </li>
|
||||
<li> Marginal distributions </li>
|
||||
<li> Conditional distributions </li>
|
||||
<li> Product of two normals </li>
|
||||
<li> Change of variable formula </li>
|
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</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine>Optimization</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing" target="_blank" rel="noreferrer">Gradient-based optimmization</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Convexity </li>
|
||||
<li> Steepest descent </li>
|
||||
<li> Newton's method </li>
|
||||
<li> Gauss-Newton method </li>
|
||||
<li> Line search </li>
|
||||
<li> Reparameterization </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank" rel="noreferrer">Bayesian optimization</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Gaussian processes </li>
|
||||
<li> Acquisition functions </li>
|
||||
<li> Incorporating noise</li>
|
||||
<li> Kernel choice </li>
|
||||
<li> Learning GP parameters </li>
|
||||
<li> Tips, tricks, and limitations </li>
|
||||
<li> Beta-Bernoulli bandit </li>
|
||||
<li> Random forests for BO </li>
|
||||
<li> Tree-Parzen estimators </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/" target="_blank" rel="noreferrer">SAT Solvers I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Boolean logic and satisfiability </li>
|
||||
<li> Conjunctive normal form </li>
|
||||
<li> The Tseitin transformation </li>
|
||||
<li> SAT and related problems </li>
|
||||
<li> SAT constructions </li>
|
||||
<li> Graph coloring and scheduling </li>
|
||||
<li> Fitting binary neural networks</li>
|
||||
<li> Fitting decision trees</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/" target="_blank" rel="noreferrer">SAT Solvers II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Conditioning </li>
|
||||
<li> Resolution </li>
|
||||
<li> Solving 2-SAT by unit propagation </li>
|
||||
<li> Directional resolution </li>
|
||||
<li> SAT as binary search </li>
|
||||
<li> DPLL </li>
|
||||
<li> Conflict driven clause learning</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank" rel="noreferrer">SAT Solvers III</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Satisfiability vs. problem size </li>
|
||||
<li> Factor graph representation </li>
|
||||
<li> Max product / sum product for SAT </li>
|
||||
<li> Survey propagation </li>
|
||||
<li> SAT with non-binary variables </li>
|
||||
<li> SMT solvers </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank" rel="noreferrer">SAT Solvers III</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Satisfiability vs. problem size </li>
|
||||
<li> Factor graph representation </li>
|
||||
<li> Max product / sum product for SAT </li>
|
||||
<li> Survey propagation </li>
|
||||
<li> SAT with non-binary variables </li>
|
||||
<li> SMT solvers </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
|
||||
<TopLine>Computer vision</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Whitening </li>
|
||||
<li> Histogram equalization </li>
|
||||
<li> Filtering </li>
|
||||
<li> Edges and corners </li>
|
||||
<li> Dimensionality reduction </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Pinhole camera model </li>
|
||||
<li> Radial distortion </li>
|
||||
<li> Homogeneous coordinates </li>
|
||||
<li> Learning extrinsic parameters </li>
|
||||
<li> Learning intrinsic parameters </li>
|
||||
<li> Inferring three-dimensional world points </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Euclidean, similarity, affine, projective transformations </li>
|
||||
<li> Fitting transformation models </li>
|
||||
<li> Inference in transformation models </li>
|
||||
<li> Three geometric problems for planes </li>
|
||||
<li> Transformations between images </li>
|
||||
<li> Robust learning of transformations </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Two view geometry </li>
|
||||
<li> The essential matrix </li>
|
||||
<li> The fundamental matrix </li>
|
||||
<li> Two-view reconstruction pipeline </li>
|
||||
<li> Rectification </li>
|
||||
<li> Multiview reconstruction </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine>Reinforcement learning</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://arxiv.org/abs/2307.05979" target="_blank" rel="noreferrer">Transformers in RL</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Challenges in RL</li>
|
||||
<li> Advantages of transformers for RL</li>
|
||||
<li> Representation learning</li>
|
||||
<li> Transition function learning</li>
|
||||
<li> Reward learning </li>
|
||||
<li> Policy learning </li>
|
||||
<li> Training strategy </li>
|
||||
<li> Interpretability </li>
|
||||
<li> Applications </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
</Column1>
|
||||
|
||||
{/* ########################################### */}
|
||||
|
||||
<Column2>
|
||||
<TopLine>AI Theory</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank" rel="noreferrer">Gradient flow</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Gradient flow </li>
|
||||
<li> Evolution of residual </li>
|
||||
<li> Evolution of parameters </li>
|
||||
<li> Evolution of model predictions </li>
|
||||
<li> Evolution of prediction covariance </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank" rel="noreferrer">Neural tangent kernel</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Infinite width neural networks </li>
|
||||
<li> Training dynamics </li>
|
||||
<li> Empirical NTK for shallow network</li>
|
||||
<li> Analytical NTK for shallow network </li>
|
||||
<li> Empirical NTK for ddep network </li>
|
||||
<li> Analtical NTK for deep network</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine>Temporal models</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank" rel="noreferrer">Temporal models</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Kalman filter </li>
|
||||
<li> Smoothing </li>
|
||||
<li> Extended Kalman filter </li>
|
||||
<li> Unscented Kalman filter </li>
|
||||
<li> Particle filtering </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine> Unsupervised learning</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing" target="_blank" rel="noreferrer">Modeling complex data densities</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Hidden variables </li>
|
||||
<li> Expectation maximization </li>
|
||||
<li> Mixture of Gaussians </li>
|
||||
<li> The t-distribution </li>
|
||||
<li> Factor analysis </li>
|
||||
<li> The EM algorithm in detail </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank" rel="noreferrer">Variational autoencoders</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Non-linear latent variable models </li>
|
||||
<li> Evidence lower bound (ELBO) </li>
|
||||
<li> ELBO properties </li>
|
||||
<li> Variational approximation </li>
|
||||
<li> The variational autoencoder </li>
|
||||
<li> Reparameterization trick </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://arxiv.org/abs/1908.09257" target="_blank" rel="noreferrer">Normalizing flows: introduction and review</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Normalizing flows </li>
|
||||
<li> Elementwise and linear flows </li>
|
||||
<li> Planar and radial flows </li>
|
||||
<li> Coupling and auto-regressive flows </li>
|
||||
<li> Coupling functions </li>
|
||||
<li> Residual flows </li>
|
||||
<li> Infinitesimal (continuous) flows </li>
|
||||
<li> Datasets and performance </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
<TopLine>Graphical Models</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing" target="_blank" rel="noreferrer">Graphical models</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Conditional independence </li>
|
||||
<li> Directed graphical models </li>
|
||||
<li> Undirected graphical models </li>
|
||||
<li> Inference in graphical models </li>
|
||||
<li> Sampling in graphical models </li>
|
||||
<li> Learning in graphical models </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing" target="_blank" rel="noreferrer">Models for chains and trees</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Hidden Markov models </li>
|
||||
<li> Viterbi algorithm </li>
|
||||
<li> Forward-backward algorithm </li>
|
||||
<li> Belief propagation </li>
|
||||
<li> Sum product algorithm </li>
|
||||
<li> Extension to trees </li>
|
||||
<li> Graphs with loops </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank" rel="noreferrer">Models for grids</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Markov random fields </li>
|
||||
<li> MAP inference in binary pairwise MRFs </li>
|
||||
<li> Graph cuts </li>
|
||||
<li> Multi-label pairwise MRFs </li>
|
||||
<li> Alpha-expansion algorithm </li>
|
||||
<li> Conditional random fields </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine>Machine learning</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing" target="_blank" rel="noreferrer">Learning and inference</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Discriminative models </li>
|
||||
<li> Generative models </li>
|
||||
<li> Example: regression </li>
|
||||
<li> Example: classification </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank" rel="noreferrer">Regression models</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Linear regression </li>
|
||||
<li> Bayesian linear regression </li>
|
||||
<li> Non-linear regression </li>
|
||||
<li> Bayesian non-linear regression </li>
|
||||
<li> The kernel trick </li>
|
||||
<li> Gaussian process regression </li>
|
||||
<li> Sparse linear regression </li>
|
||||
<li> Relevance vector regression </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank" rel="noreferrer">Classification models</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Logistic regression </li>
|
||||
<li> Bayesian logistic regression </li>
|
||||
<li> Non-linear logistic regression </li>
|
||||
<li> Gaussian process classification </li>
|
||||
<li> Relevance vector classification </li>
|
||||
<li> Incremental fitting: boosting and trees </li>
|
||||
<li> Multi-class logistic regression </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/" target="_blank" rel="noreferrer">Few-shot learning and meta-learning I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Meta-learning framework </li>
|
||||
<li> Approaches to meta-learning </li>
|
||||
<li> Matching networks </li>
|
||||
<li> Prototypical networks </li>
|
||||
<li> Relation networks </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-3-few-shot-learning-and-meta-learning-ii/" target="_blank" rel="noreferrer">Few-shot learning and meta-learning II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> MAML & Reptile </li>
|
||||
<li> LSTM based meta-learning </li>
|
||||
<li> Reinforcement learning based approaches</li>
|
||||
<li> Memory augmented neural networks </li>
|
||||
<li> SNAIL </li>
|
||||
<li> Generative models </li>
|
||||
<li> Data augmentation approaches </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
<TopLine>Natural language processing</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/" target="_blank" rel="noreferrer">Neural natural language generation I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Encoder-decoder architecture </li>
|
||||
<li> Maximum-likelihood training </li>
|
||||
<li> Greedy search </li>
|
||||
<li> Beam search </li>
|
||||
<li> Diverse beam search </li>
|
||||
<li> Top-k sampling </li>
|
||||
<li> Nucleus sampling </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-7-neural-natural-language-generation-sequence-level-training/" target="_blank" rel="noreferrer">Neural natural language generation II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Fine-tuning with reinforcement learning </li>
|
||||
<li> Training from scratch with RL </li>
|
||||
<li> RL vs. structured prediction </li>
|
||||
<li> Minimum risk training </li>
|
||||
<li> Scheduled sampling </li>
|
||||
<li> Beam search optimization </li>
|
||||
<li> SeaRNN </li>
|
||||
<li> Reward-augmented maximum likelihood </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/" target="_blank" rel="noreferrer">Parsing I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Parse trees </li>
|
||||
<li> Context-free grammars </li>
|
||||
<li> Chomsky normal form </li>
|
||||
<li> CYK recognition algorithm </li>
|
||||
<li> Worked example </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/" target="_blank" rel="noreferrer">Parsing II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Weighted context-free grammars </li>
|
||||
<li> Semirings </li>
|
||||
<li> Inside algorithm </li>
|
||||
<li> Inside weights </li>
|
||||
<li> Weighted parsing </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/" target="_blank" rel="noreferrer">Parsing III</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Probabilistic context-free grammars </li>
|
||||
<li> Parameter estimation (supervised) </li>
|
||||
<li> Parameter estimation (unsupervised) </li>
|
||||
<li> Viterbi training </li>
|
||||
<li> Expectation maximization </li>
|
||||
<li> Outside from inside </li>
|
||||
<li> Interpretation of outside weights </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank" rel="noreferrer">XLNet</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Language modeling </li>
|
||||
<li> XLNet training objective </li>
|
||||
<li> Permutations </li>
|
||||
<li> Attention mask </li>
|
||||
<li> Two stream self-attention </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
|
||||
|
||||
|
||||
<TopLine>Responsible AI</TopLine>
|
||||
<MoreOuterList>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/" target="_blank" rel="noreferrer">Bias and fairness</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Sources of bias</li>
|
||||
<li> Demographic Parity </li>
|
||||
<li> Equality of odds</li>
|
||||
<li> Equality of opportunity </li>
|
||||
<li> Individual fairness</li>
|
||||
<li> Bias mitigation</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank" rel="noreferrer">Explainability I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Taxonomy of XAI approaches</li>
|
||||
<li> Local post-hoc explanations </li>
|
||||
<li> Individual conditional explanation</li>
|
||||
<li> Counterfactual explanations</li>
|
||||
<li> LIME & Anchors</li>
|
||||
<li> Shapley additive explanations & SHAP</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/" target="_blank" rel="noreferrer">Explainability II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Global feature importance</li>
|
||||
<li> Partial dependence & ICE plots</li>
|
||||
<li> Accumulated local effects</li>
|
||||
<li> Aggregate SHAP values</li>
|
||||
<li> Prototypes & criticisms</li>
|
||||
<li> Surrogate / proxy models</li>
|
||||
<li> Inherently interpretable models</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/" target="_blank" rel="noreferrer">Differential privacy I</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Early approaches to privacy </li>
|
||||
<li> Fundamental law of information recovery </li>
|
||||
<li> Differential privacy</li>
|
||||
<li> Properties of differential privacy </li>
|
||||
<li> The Laplace mechanism</li>
|
||||
<li> Examples</li>
|
||||
<li> Other mechanisms and definitions</li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/" target="_blank" rel="noreferrer">Differential privacy II</a>
|
||||
<MoreInnerP>
|
||||
<MoreInnerList>
|
||||
<li> Differential privacy and matchine learning</li>
|
||||
<li> DPSGD</li>
|
||||
<li> PATE </li>
|
||||
<li> Differentially private data generation</li>
|
||||
<li> DPGAN</li>
|
||||
<li> PateGAN </li>
|
||||
</MoreInnerList>
|
||||
</MoreInnerP>
|
||||
</li>
|
||||
</MoreOuterList>
|
||||
</Column2>
|
||||
</MoreRow2>
|
||||
</MoreWrapper>
|
||||
</MoreContainer>
|
||||
</>
|
||||
)
|
||||
}
|
||||
|
||||
export default MoreSection
|
||||
|
||||
|
||||
Reference in New Issue
Block a user