Merge branch 'main' into cleanup
This commit is contained in:
Tom Heaton
File diff suppressed because one or more lines are too long
@@ -337,8 +337,8 @@
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{
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"cell_type": "code",
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"source": [
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"# You can see that the values of the hidden units are increasing on average (the variance is across all hidden units at the layer\n",
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"# and the 1000 training examples\n",
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"# You can see that the gradients of the hidden units are increasing on average (the standard deviation is across all hidden units at the layer\n",
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"# and the 100 training examples\n",
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"\n",
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"# TO DO\n",
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"# Change this to 50 layers with 80 hidden units per layer\n",
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@@ -61,7 +61,7 @@
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"by drawing $I$ samples $y_i$ and using the formula:\n",
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"\n",
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"\\begin{equation}\n",
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"\\mathbb{E}_{y}\\Bigl[\\exp\\bigl[- (y-1)^4\\bigr]\\Bigr] \\approx \\frac{1}{I} \\sum_{i=1}^I \\exp\\bigl[-(y-1)^4 \\bigr]\n",
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"\\mathbb{E}_{y}\\Bigl[\\exp\\bigl[- (y-1)^4\\bigr]\\Bigr] \\approx \\frac{1}{I} \\sum_{i=1}^I \\exp\\bigl[-(y_i-1)^4 \\bigr]\n",
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"\\end{equation}"
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]
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},
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@@ -39,6 +39,16 @@ export default function HeroSection() {
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<HeroNewsBlock>
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<HeroNewsTitle>RECENT NEWS:</HeroNewsTitle>
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<HeroNewsItem>
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<HeroNewsItemDate>05/22/24</HeroNewsItemDate>
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<HeroNewsItemContent> New <UDLLink href="https://www.borealisai.com/research-blogs/neural-tangent-kernel-applications/"> blog </UDLLink> about the applications of the neural tangent kernel.</HeroNewsItemContent>
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</HeroNewsItem>
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<HeroNewsItem>
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<HeroNewsItemDate>05/10/24</HeroNewsItemDate>
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<HeroNewsItemContent> Positive <UDLLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">review</UDLLink> in Nature Machine Intelligence.</HeroNewsItemContent>
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</HeroNewsItem>
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{/* <HeroNewsItem>
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<HeroNewsItemDate>03/12/24</HeroNewsItemDate>
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<HeroNewsItemContent>
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{" "}
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@@ -53,8 +63,14 @@ export default function HeroSection() {
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Neural Tangent Kernel.
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</UDLLink>
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</HeroNewsItemContent>
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</HeroNewsItem>
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<HeroNewsItemContent> Book now available again.</HeroNewsItemContent>
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</HeroNewsItem> */}
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<HeroNewsItem>
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<HeroNewsItemDate>02/21/24</HeroNewsItemDate>
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<HeroNewsItemContent>New blog about the <UDLLink href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/">Neural Tangent Kernel</UDLLink>.</HeroNewsItemContent>
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</HeroNewsItem>
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{/* <HeroNewsItem>
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<HeroNewsItemDate>02/15/24</HeroNewsItemDate>
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<HeroNewsItemContent>
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{" "}
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@@ -62,6 +78,9 @@ export default function HeroSection() {
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printing available mid-March.
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</HeroNewsItemContent>
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</HeroNewsItem>
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<HeroNewsItemContent> First printing of book has sold out in most places. Second printing available mid-March.</HeroNewsItemContent>
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</HeroNewsItem> */}
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<HeroNewsItem>
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<HeroNewsItemDate>01/29/24</HeroNewsItemDate>
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<HeroNewsItemContent>
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@@ -98,14 +117,7 @@ export default function HeroSection() {
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</HeroNewsItem>
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<HeroNewsItem>
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<HeroNewsItemDate>12/06/23</HeroNewsItemDate>
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<HeroNewsItemContent>
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{" "}
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I did an{" "}
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<UDLLink href="https://www.borealisai.com/news/understanding-deep-learning/">
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interview
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</UDLLink>{" "}
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discussing the book with Borealis AI.
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</HeroNewsItemContent>
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<HeroNewsItemContent> <UDLLink href="https://www.borealisai.com/news/understanding-deep-learning/">Interview</UDLLink> with Borealis AI.</HeroNewsItemContent>
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</HeroNewsItem>
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<HeroNewsItem>
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<HeroNewsItemDate>12/05/23</HeroNewsItemDate>
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@@ -140,22 +152,11 @@ export default function HeroSection() {
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<HeroImgWrap>
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<Img src={img} alt="book cover" />
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</HeroImgWrap>
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<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v2.05/UnderstandingDeepLearning_04_18_24_C.pdf">
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Download full pdf (18 Apr 2024)
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</HeroLink>
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<HeroDownloadsImg
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src="https://img.shields.io/github/downloads/udlbook/udlbook/total"
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alt="download stats shield"
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/>
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<HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">
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Buy the book
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</HeroLink>
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<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">
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Answers to selected questions
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</HeroLink>
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<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">
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Errata
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</HeroLink>
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<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v4.0.1/UnderstandingDeepLearning_05_27_24_C.pdf">Download full pdf (27 May 2024)</HeroLink>
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<HeroDownloadsImg src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield"/>
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<HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">Buy the book</HeroLink>
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<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">Answers to selected questions</HeroLink>
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<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">Errata</HeroLink>
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</HeroColumn2>
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</HeroRow>
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</HeroContent>
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@@ -76,6 +76,8 @@ export default function MediaSection() {
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<TopLine>Reviews</TopLine>
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<MediaContent>
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<ul>
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<li> Nature Machine Intelligence <MediaLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf"> review </MediaLink> by <MediaLink href="https://wang-axis.github.io/">Ge Wang</MediaLink></li>
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<li>
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{" "}
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Amazon{" "}
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@@ -96,6 +98,9 @@ export default function MediaSection() {
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</MediaLink>{" "}
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by Vishal V.
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</li>
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<li> Amazon <MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">reviews</MediaLink></li>
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<li>Goodreads <MediaLink href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">reviews </MediaLink></li>
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<li>Book <MediaLink href="https://medium.com/@vishalvignesh/udl-book-review-the-new-deep-learning-textbook-youll-want-to-finish-69e1557b018d">review</MediaLink> by Vishal V.</li>
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</ul>
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</MediaContent>
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</Column1>
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@@ -408,7 +408,7 @@ export default function MoreSection() {
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</MoreInnerP>
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</li>
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</MoreOuterList>
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<li>
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{/* <li>
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<MoreLink
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href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/"
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target="_blank"
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@@ -481,7 +481,56 @@ export default function MoreSection() {
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{" "}
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Euclidean, similarity, affine, projective
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transformations{" "}
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</li>
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</li> */}
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<TopLine>Temporal models</TopLine>
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<MoreOuterList>
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<li>
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<MoreLink href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank" rel="noreferrer">Temporal models</MoreLink>
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<MoreInnerP>
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<MoreInnerList>
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<li> Kalman filter </li>
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<li> Smoothing </li>
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<li> Extended Kalman filter </li>
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<li> Unscented Kalman filter </li>
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<li> Particle filtering </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>Computer vision</TopLine>
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<MoreOuterList>
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<li>
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<MoreLink href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</MoreLink>
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<MoreInnerP>
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<MoreInnerList>
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<li> Whitening </li>
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<li> Histogram equalization </li>
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<li> Filtering </li>
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<li> Edges and corners </li>
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<li> Dimensionality reduction </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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<MoreLink href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</MoreLink>
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<MoreInnerP>
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<MoreInnerList>
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<li> Pinhole camera model </li>
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<li> Radial distortion </li>
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<li> Homogeneous coordinates </li>
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<li> Learning extrinsic parameters </li>
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<li> Learning intrinsic parameters </li>
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<li> Inferring three-dimensional world points </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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<MoreLink href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</MoreLink>
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<MoreInnerP>
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<MoreInnerList>
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<li> Euclidean, similarity, affine, projective transformations </li>
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<li> Fitting transformation models </li>
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<li> Inference in transformation models </li>
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<li> Three geometric problems for planes </li>
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@@ -575,16 +624,13 @@ export default function MoreSection() {
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<li> Training dynamics </li>
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<li> Empirical NTK for shallow network</li>
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<li> Analytical NTK for shallow network </li>
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<li> Empirical NTK for ddep network </li>
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<li> Empirical NTK for deep network </li>
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<li> Analtical NTK for deep 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>Temporal models</TopLine>
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<MoreOuterList>
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<li>
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{/*
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<MoreLink
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href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing"
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target="_blank"
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@@ -598,12 +644,23 @@ export default function MoreSection() {
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<li> Smoothing </li>
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<li> Extended Kalman filter </li>
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<li> Unscented Kalman filter </li>
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<li> Particle filtering </li>
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<li> Particle filtering </li>*/}
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<MoreLink href="https://www.borealisai.com/research-blogs/neural-tangent-kernel-applications/" target="_blank" rel="noreferrer">NTK applications</MoreLink>
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<MoreInnerP>
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<MoreInnerList>
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<li> Trainability </li>
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<li> Convergence bounds </li>
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<li> Evolution of parameters</li>
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<li> Evolution of predictions </li>
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<li> NTK Gaussian processes</li>
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<li> NTK and generalizability</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> Unsupervised learning</TopLine>
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<MoreOuterList>
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<li>
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