Compare commits
10 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
|
14b751ff47 | ||
|
|
80e99ef2da | ||
|
|
46214f64bc | ||
|
|
c875fb0361 | ||
|
|
451ccc0832 | ||
|
|
4b939b7426 | ||
|
|
2d300a16a1 | ||
|
|
d057548be9 | ||
|
|
75976a32d0 | ||
|
|
48b204df2c |
@@ -4,7 +4,6 @@
|
|||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {
|
"colab": {
|
||||||
"provenance": [],
|
"provenance": [],
|
||||||
"authorship_tag": "ABX9TyOaATWBrwVMylV1akcKtHjt",
|
|
||||||
"include_colab_link": true
|
"include_colab_link": true
|
||||||
},
|
},
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
@@ -250,7 +249,7 @@
|
|||||||
"# Main backward pass routine\n",
|
"# Main backward pass routine\n",
|
||||||
"def backward_pass(all_weights, all_biases, all_f, all_h, y):\n",
|
"def backward_pass(all_weights, all_biases, all_f, all_h, y):\n",
|
||||||
" # Retrieve number of layers\n",
|
" # Retrieve number of layers\n",
|
||||||
" K = all_weights\n",
|
" K = len(all_weights) - 1\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
|
" # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
|
||||||
" all_dl_dweights = [None] * (K+1)\n",
|
" all_dl_dweights = [None] * (K+1)\n",
|
||||||
|
|||||||
@@ -4,7 +4,6 @@
|
|||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {
|
"colab": {
|
||||||
"provenance": [],
|
"provenance": [],
|
||||||
"authorship_tag": "ABX9TyOMSGUFWT+YN0fwYHpMmHJM",
|
|
||||||
"include_colab_link": true
|
"include_colab_link": true
|
||||||
},
|
},
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
@@ -99,7 +98,7 @@
|
|||||||
"\n",
|
"\n",
|
||||||
"# TODO -- Define node matrix\n",
|
"# TODO -- Define node matrix\n",
|
||||||
"# There will be 9 nodes and 118 possible chemical elements\n",
|
"# There will be 9 nodes and 118 possible chemical elements\n",
|
||||||
"# so we'll define a 9x118 matrix. Each column represents one\n",
|
"# so we'll define a 118x9 matrix. Each column represents one\n",
|
||||||
"# node and is a one-hot vector (i.e. all zeros, except a single one at the\n",
|
"# node and is a one-hot vector (i.e. all zeros, except a single one at the\n",
|
||||||
"# chemical number of the element).\n",
|
"# chemical number of the element).\n",
|
||||||
"# Chemical numbers: Hydrogen-->1, Carbon-->6, Oxygen-->8\n",
|
"# Chemical numbers: Hydrogen-->1, Carbon-->6, Oxygen-->8\n",
|
||||||
@@ -241,4 +240,4 @@
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
@@ -218,7 +218,8 @@
|
|||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"source": [
|
"source": [
|
||||||
"was = np.sum(TP * dist_mat)\n",
|
"was = np.sum(TP * dist_mat)\n",
|
||||||
"print(\"Wasserstein distance = \", was)"
|
"print(\"Your Wasserstein distance = \", was)\n",
|
||||||
|
"print(\"Correct answer = 0.15148578811369506\")"
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"id": "yiQ_8j-Raq3c"
|
"id": "yiQ_8j-Raq3c"
|
||||||
|
|||||||
Binary file not shown.
Binary file not shown.
BIN
UDL_Errata.pdf
BIN
UDL_Errata.pdf
Binary file not shown.
406
index_old.html
406
index_old.html
@@ -1,406 +0,0 @@
|
|||||||
<!DOCTYPE html>
|
|
||||||
<html lang="en">
|
|
||||||
<head>
|
|
||||||
<meta charset="UTF-8">
|
|
||||||
<title>udlbook</title>
|
|
||||||
<link rel="stylesheet" href="style.css">
|
|
||||||
</head>
|
|
||||||
|
|
||||||
<body>
|
|
||||||
<div id="head">
|
|
||||||
<div>
|
|
||||||
<h1 style="margin: 0; font-size: 36px">Understanding Deep Learning</h1>
|
|
||||||
by Simon J.D. Prince
|
|
||||||
<br>Published by MIT Press Dec 5th 2023.<br>
|
|
||||||
<ul>
|
|
||||||
<li>
|
|
||||||
<p style="font-size: larger; margin-bottom: 0">Download full PDF <a
|
|
||||||
href="https://github.com/udlbook/udlbook/releases/download/v2.03/UnderstandingDeepLearning_02_26_24_C.pdf">here</a>
|
|
||||||
</p>2024-03-26. CC-BY-NC-ND license<br>
|
|
||||||
<img src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield">
|
|
||||||
</li>
|
|
||||||
<li> Order your copy from <a href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">here </a></li>
|
|
||||||
<li> Known errata can be found here: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">PDF</a></li>
|
|
||||||
<li> Report new errata via <a href="https://github.com/udlbook/udlbook/issues">github</a>
|
|
||||||
or contact me directly at udlbookmail@gmail.com
|
|
||||||
<li> Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a
|
|
||||||
href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
|
|
||||||
</ul>
|
|
||||||
<h2>Table of contents</h2>
|
|
||||||
<ul>
|
|
||||||
<li> Chapter 1 - Introduction
|
|
||||||
<li> Chapter 2 - Supervised learning
|
|
||||||
<li> Chapter 3 - Shallow neural networks
|
|
||||||
<li> Chapter 4 - Deep neural networks
|
|
||||||
<li> Chapter 5 - Loss functions
|
|
||||||
<li> Chapter 6 - Training models
|
|
||||||
<li> Chapter 7 - Gradients and initialization
|
|
||||||
<li> Chapter 8 - Measuring performance
|
|
||||||
<li> Chapter 9 - Regularization
|
|
||||||
<li> Chapter 10 - Convolutional networks
|
|
||||||
<li> Chapter 11 - Residual networks
|
|
||||||
<li> Chapter 12 - Transformers
|
|
||||||
<li> Chapter 13 - Graph neural networks
|
|
||||||
<li> Chapter 14 - Unsupervised learning
|
|
||||||
<li> Chapter 15 - Generative adversarial networks
|
|
||||||
<li> Chapter 16 - Normalizing flows
|
|
||||||
<li> Chapter 17 - Variational autoencoders
|
|
||||||
<li> Chapter 18 - Diffusion models
|
|
||||||
<li> Chapter 19 - Deep reinforcement learning
|
|
||||||
<li> Chapter 20 - Why does deep learning work?
|
|
||||||
<li> Chapter 21 - Deep learning and ethics
|
|
||||||
</ul>
|
|
||||||
</div>
|
|
||||||
<div id="cover">
|
|
||||||
<img src="https://raw.githubusercontent.com/udlbook/udlbook/main/UDLCoverSmall.jpg"
|
|
||||||
alt="front cover">
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
<div id="body">
|
|
||||||
<h2>Resources for instructors </h2>
|
|
||||||
<p>Instructor answer booklet available with proof of credentials via <a
|
|
||||||
href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning"> MIT Press</a>.</p>
|
|
||||||
<p>Request an exam/desk copy via <a href="https://mitpress.ublish.com/request?cri=15055">MIT Press</a>.</p>
|
|
||||||
<p>Figures in PDF (vector) / SVG (vector) / Powerpoint (images):
|
|
||||||
<ul>
|
|
||||||
<li> Chapter 1 - Introduction: <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 2 - Supervised learning: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 3 - Shallow neural networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 4 - Deep neural networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 5 - Loss functions: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 6 - Training models: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 7 - Gradients and initialization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 8 - Measuring performance: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 9 - Regularization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 10 - Convolutional networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 11 - Residual networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 12 - Transformers: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 13 - Graph neural networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 14 - Unsupervised learning: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
|
||||||
PowerPoint Figures</a>
|
|
||||||
<li> Chapter 15 - Generative adversarial networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 16 - Normalizing flows: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 17 - Variational autoencoders: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Chapter 18 - Diffusion models: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
|
||||||
PowerPoint Figures</a>
|
|
||||||
<li> Chapter 19 - Deep reinforcement learning: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
|
||||||
PowerPoint Figures </a>
|
|
||||||
<li> Chapter 20 - Why does deep learning work?: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG Figures</a>
|
|
||||||
/
|
|
||||||
<a href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
|
||||||
PowerPoint Figures</a>
|
|
||||||
<li> Chapter 21 - Deep learning and ethics: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF Figures</a> / <a
|
|
||||||
href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU"> SVG Figures</a>/
|
|
||||||
<a
|
|
||||||
href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
|
|
||||||
Figures</a>
|
|
||||||
<li> Appendices - <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF
|
|
||||||
Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
|
|
||||||
SVG
|
|
||||||
Figures</a> / <a
|
|
||||||
href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">Powerpoint
|
|
||||||
Figures</a>
|
|
||||||
</ul>
|
|
||||||
|
|
||||||
Instructions for editing figures / equations can be found <a
|
|
||||||
href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">here</a>.
|
|
||||||
|
|
||||||
<p> My slides for 20 lecture undergraduate deep learning course:</p>
|
|
||||||
<ul>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">1. Introduction</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">2. Supervised Learning</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">3. Shallow Neural Networks</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">4. Deep Neural Networks</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">5. Loss Functions</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">6. Fitting Models</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">7. Computing Gradients</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">7b. Initialization</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">8. Performance</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">9. Regularization</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">10. Convolutional Networks</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">11. Image Generation</a></li>
|
|
||||||
<li><a href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">12. Transformers and LLMs</a></li>
|
|
||||||
</ul>
|
|
||||||
|
|
||||||
<h2>Resources for students</h2>
|
|
||||||
|
|
||||||
<p>Answers to selected questions: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">PDF</a>
|
|
||||||
</p>
|
|
||||||
<p>Python notebooks: (Early ones more thoroughly tested than later ones!)</p>
|
|
||||||
|
|
||||||
<ul>
|
|
||||||
<li> Notebook 1.1 - Background mathematics: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 2.1 - Supervised learning: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 3.1 - Shallow networks I: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 3.2 - Shallow networks II: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 3.3 - Shallow network regions: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 3.4 - Activation functions: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 4.1 - Composing networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 4.2 - Clipping functions: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 4.3 - Deep networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 5.1 - Least squares loss: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 5.2 - Binary cross-entropy loss: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 5.3 - Multiclass cross-entropy loss: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 6.1 - Line search: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 6.2 - Gradient descent: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 6.3 - Stochastic gradient descent: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 6.4 - Momentum: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 6.5 - Adam: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 7.1 - Backpropagation in toy model: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 7.2 - Backpropagation: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 7.3 - Initialization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 8.1 - MNIST-1D performance: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 8.2 - Bias-variance trade-off: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 8.3 - Double descent: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 8.4 - High-dimensional spaces: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 9.1 - L2 regularization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 9.2 - Implicit regularization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 9.3 - Ensembling: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 9.4 - Bayesian approach: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 9.5 - Augmentation <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 10.1 - 1D convolution: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 10.2 - Convolution for MNIST-1D: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 10.3 - 2D convolution: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 10.4 - Downsampling & upsampling: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 10.5 - Convolution for MNIST: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 11.1 - Shattered gradients: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 11.2 - Residual networks: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 11.3 - Batch normalization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 12.1 - Self-attention: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 12.2 - Multi-head self-attention: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 12.3 - Tokenization: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 12.4 - Decoding strategies: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 13.1 - Encoding graphs: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 13.2 - Graph classification : <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 13.3 - Neighborhood sampling: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 13.4 - Graph attention: <a
|
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </a>
|
|
||||||
</li>
|
|
||||||
<li> Notebook 15.1 - GAN toy example: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 15.2 - Wasserstein distance: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 16.1 - 1D normalizing flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 16.2 - Autoregressive flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 16.3 - Contraction mappings: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 17.1 - Latent variable models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 17.2 - Reparameterization trick: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 17.3 - Importance sampling: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 18.1 - Diffusion encoder: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 18.2 - 1D diffusion model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 18.3 - Reparameterized model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 18.4 - Families of diffusion models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 19.1 - Markov decision processes: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 19.2 - Dynamic programming: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 19.3 - Monte-Carlo methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 19.4 - Temporal difference methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 19.5 - Control variates: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 20.1 - Random data: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 20.2 - Full-batch gradient descent: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 20.3 - Lottery tickets: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 20.4 - Adversarial attacks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 21.1 - Bias mitigation: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </a></li>
|
|
||||||
<li> Notebook 21.2 - Explainability: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </a></li>
|
|
||||||
</ul>
|
|
||||||
|
|
||||||
|
|
||||||
<br>
|
|
||||||
<h2>Citation</h2>
|
|
||||||
<pre><code>
|
|
||||||
@book{prince2023understanding,
|
|
||||||
author = "Simon J.D. Prince",
|
|
||||||
title = "Understanding Deep Learning",
|
|
||||||
publisher = "MIT Press",
|
|
||||||
year = 2023,
|
|
||||||
url = "http://udlbook.com"
|
|
||||||
}
|
|
||||||
</code></pre>
|
|
||||||
</div>
|
|
||||||
</body>
|
|
||||||
@@ -2,7 +2,7 @@
|
|||||||
"name": "react-website-smooth-scroll",
|
"name": "react-website-smooth-scroll",
|
||||||
"version": "0.1.0",
|
"version": "0.1.0",
|
||||||
"private": true,
|
"private": true,
|
||||||
"homepage": "https://udlbook.github.io/udlbook/",
|
"homepage": "https://udlbook.github.io/udlbook",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"@fortawesome/fontawesome-svg-core": "^6.5.1",
|
"@fortawesome/fontawesome-svg-core": "^6.5.1",
|
||||||
"@testing-library/jest-dom": "^5.15.1",
|
"@testing-library/jest-dom": "^5.15.1",
|
||||||
|
|||||||
BIN
public/favicon.ico
Executable file → Normal file
BIN
public/favicon.ico
Executable file → Normal file
Binary file not shown.
|
Before Width: | Height: | Size: 3.8 KiB After Width: | Height: | Size: 15 KiB |
@@ -27,7 +27,7 @@
|
|||||||
<link rel="preconnect" href="https://fonts.googleapis.com">
|
<link rel="preconnect" href="https://fonts.googleapis.com">
|
||||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
|
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
|
||||||
<link href="https://fonts.googleapis.com/css2?family=Encode+Sans+Expanded:wght@400;700&display=swap" rel="stylesheet">
|
<link href="https://fonts.googleapis.com/css2?family=Encode+Sans+Expanded:wght@400;700&display=swap" rel="stylesheet">
|
||||||
<title>React App</title>
|
<title>Understanding Deep Learning</title>
|
||||||
</head>
|
</head>
|
||||||
<body>
|
<body>
|
||||||
<noscript>You need to enable JavaScript to run this app.</noscript>
|
<noscript>You need to enable JavaScript to run this app.</noscript>
|
||||||
|
|||||||
@@ -9,7 +9,7 @@ function App() {
|
|||||||
return (
|
return (
|
||||||
<Router>
|
<Router>
|
||||||
<Routes>
|
<Routes>
|
||||||
<Route exact path="/" element ={<Home/>} />
|
<Route exact path="/udlbook/" element ={<Home/>} />
|
||||||
</Routes>
|
</Routes>
|
||||||
|
|
||||||
</Router>
|
</Router>
|
||||||
|
|||||||
@@ -105,6 +105,9 @@ export const SocialLogo = styled(Link)`
|
|||||||
align-items: center;
|
align-items: center;
|
||||||
margin-bottom: 16px;
|
margin-bottom: 16px;
|
||||||
font-weight: bold;
|
font-weight: bold;
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 20px;
|
||||||
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
export const WebsiteRights = styled.small`
|
export const WebsiteRights = styled.small`
|
||||||
|
|||||||
@@ -16,7 +16,7 @@ const Footer = () => {
|
|||||||
<FooterWrap>
|
<FooterWrap>
|
||||||
<SocialMedia>
|
<SocialMedia>
|
||||||
<SocialMediaWrap>
|
<SocialMediaWrap>
|
||||||
<SocialLogo to='/' onClick={toggleHome}>
|
<SocialLogo to='/udlbook/' onClick={toggleHome}>
|
||||||
Understanding Deep Learning
|
Understanding Deep Learning
|
||||||
</SocialLogo>
|
</SocialLogo>
|
||||||
<WebsiteRights>©{new Date().getFullYear()} Simon J.D. Prince</WebsiteRights>
|
<WebsiteRights>©{new Date().getFullYear()} Simon J.D. Prince</WebsiteRights>
|
||||||
|
|||||||
@@ -84,32 +84,29 @@ export const HeroNewsItem = styled.div`
|
|||||||
margin-bottom: 16px;
|
margin-bottom: 16px;
|
||||||
display: flex;
|
display: flex;
|
||||||
justify-content: start;
|
justify-content: start;
|
||||||
|
|
||||||
`
|
`
|
||||||
export const HeroNewsItemDate = styled.div`
|
export const HeroNewsItemDate = styled.div`
|
||||||
width: 20%;
|
width: 20%;
|
||||||
font-size: 16px ;
|
|
||||||
margin-right: 20px ;
|
margin-right: 20px ;
|
||||||
|
|
||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
font-size: 24px;
|
font-size: 12px;
|
||||||
}
|
}
|
||||||
|
|
||||||
@media screen and (max-width: 480px) {
|
@media screen and (max-width: 480px) {
|
||||||
font-size: 18px;
|
font-size: 12px;
|
||||||
}
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
export const HeroNewsItemContent = styled.div`
|
export const HeroNewsItemContent = styled.div`
|
||||||
width: 80%;
|
width: 80%;
|
||||||
color: #000000;
|
color: #000000;
|
||||||
font-size: 16px ;
|
|
||||||
|
|
||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
font-size: 24px;
|
font-size: 12px;
|
||||||
}
|
}
|
||||||
|
|
||||||
@media screen and (max-width: 480px) {
|
@media screen and (max-width: 480px) {
|
||||||
font-size: 18px;
|
font-size: 12px;
|
||||||
}
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
@@ -167,20 +164,89 @@ export const HeroDownloadsImg = styled.img`
|
|||||||
export const HeroLink = styled.a`
|
export const HeroLink = styled.a`
|
||||||
color: #fff;
|
color: #fff;
|
||||||
text-decoration: none;
|
text-decoration: none;
|
||||||
padding: 0.1rem 0rem;
|
padding: 0.6rem 0rem 0rem 0rem;
|
||||||
height: 100%;
|
|
||||||
cursor: pointer;
|
cursor: pointer;
|
||||||
|
position:relative ;
|
||||||
|
|
||||||
&:hover {
|
&:before{
|
||||||
filter: brightness(0.85);
|
position: absolute;
|
||||||
}
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #fff;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
&.active {
|
&:hover:before {
|
||||||
color: #000
|
opacity: 1;
|
||||||
border-bottom: 3px solid #01bf71;
|
-webkit-transform: scaleX(1.05);
|
||||||
}
|
}
|
||||||
`;
|
`;
|
||||||
|
|
||||||
|
// color: #fff;
|
||||||
|
// text-decoration: none;
|
||||||
|
// padding: 0.1rem 0rem;
|
||||||
|
// height: 100%;
|
||||||
|
// cursor: pointer;
|
||||||
|
// position:relative ;
|
||||||
|
|
||||||
|
// &:before{
|
||||||
|
// position: absolute;
|
||||||
|
// margin: 0 auto;
|
||||||
|
// top: 100%;
|
||||||
|
// left: 0;
|
||||||
|
// width: 100%;
|
||||||
|
// height: 2px;
|
||||||
|
// background-color: #000;
|
||||||
|
// content: '';
|
||||||
|
// opacity: .3;
|
||||||
|
// -webkit-transform: scaleX(1);
|
||||||
|
// transition-property: opacity, -webkit-transform;
|
||||||
|
// transition-duration: .3s;
|
||||||
|
// }
|
||||||
|
|
||||||
|
// &:hover:before {
|
||||||
|
// opacity: 1;
|
||||||
|
// -webkit-transform: scaleX(1.05);
|
||||||
|
// }
|
||||||
|
// `;
|
||||||
|
|
||||||
|
export const UDLLink = styled.a`
|
||||||
|
text-decoration: none;
|
||||||
|
color: #000;
|
||||||
|
font-weight: 300;
|
||||||
|
margin: 0 2px;
|
||||||
|
position: relative;
|
||||||
|
|
||||||
|
&:before{
|
||||||
|
position: absolute;
|
||||||
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #000;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
|
&:hover:before {
|
||||||
|
opacity: 1;
|
||||||
|
-webkit-transform: scaleX(1.05);
|
||||||
|
}
|
||||||
|
`
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
export const HeroNewsTitle = styled.div`
|
export const HeroNewsTitle = styled.div`
|
||||||
margin-left: 0px;
|
margin-left: 0px;
|
||||||
@@ -233,10 +299,6 @@ export const HeroCitationBlock = styled.div`
|
|||||||
|
|
||||||
export const HeroFollowBlock = styled.div`
|
export const HeroFollowBlock = styled.div`
|
||||||
@media screen and (max-width: 768px) {
|
@media screen and (max-width: 768px) {
|
||||||
font-size: 24px;
|
font-size: 14px;
|
||||||
}
|
}
|
||||||
|
`
|
||||||
@media screen and (max-width: 480px) {
|
|
||||||
font-size: 18px;
|
|
||||||
}
|
|
||||||
`
|
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
import React from 'react'
|
import React from 'react'
|
||||||
import { HeroContainer, HeroNewsBlock, HeroCitationBlock, HeroCitationTitle, HeroFollowBlock, HeroDownloadsImg, HeroLink, HeroRow, HeroColumn1, HeroColumn2, HeroContent, Img, HeroImgWrap, HeroNewsTitle, HeroNewsItem, HeroNewsItemDate, HeroNewsItemContent} from './HeroElements'
|
import { HeroContainer, HeroNewsBlock, HeroCitationBlock, HeroCitationTitle, HeroFollowBlock, HeroDownloadsImg, HeroLink, HeroRow, HeroColumn1, HeroColumn2, HeroContent, Img, HeroImgWrap, HeroNewsTitle, HeroNewsItem, HeroNewsItemDate, HeroNewsItemContent, UDLLink} from './HeroElements'
|
||||||
import img from '../../images/F23.prince.learning.turquoise.jpg'
|
import img from '../../images/F23.prince.learning.turquoise.jpg'
|
||||||
|
|
||||||
const HeroSection = () => {
|
const HeroSection = () => {
|
||||||
@@ -27,42 +27,41 @@ const HeroSection = () => {
|
|||||||
<HeroNewsItemContent> Book now available again.</HeroNewsItemContent>
|
<HeroNewsItemContent> Book now available again.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
|
<HeroNewsItem>
|
||||||
|
<HeroNewsItemDate>02/21/24</HeroNewsItemDate>
|
||||||
|
<HeroNewsItemContent>New blog about the <UDLLink href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/">Neural Tangent Kernel.</UDLLink></HeroNewsItemContent>
|
||||||
|
</HeroNewsItem>
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>02/15/24</HeroNewsItemDate>
|
<HeroNewsItemDate>02/15/24</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent> First printing of book has sold out in most places. Second printing available mid-March.</HeroNewsItemContent>
|
<HeroNewsItemContent> First printing of book has sold out in most places. Second printing available mid-March.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
|
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>01/29/24</HeroNewsItemDate>
|
<HeroNewsItemDate>01/29/24</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent> New blog about <a href="https://www.borealisai.com/research-blogs/gradient-flow/"> gradient flow </a> published.</HeroNewsItemContent>
|
<HeroNewsItemContent> New blog about <UDLLink href="https://www.borealisai.com/research-blogs/gradient-flow/"> gradient flow </UDLLink> published.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>12/26/23</HeroNewsItemDate>
|
<HeroNewsItemDate>12/26/23</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent> Machine Learning Street Talk <a href="https://www.youtube.com/watch?v=sJXn4Cl4oww"> podcast </a> discussing book.</HeroNewsItemContent>
|
<HeroNewsItemContent> Machine Learning Street Talk <UDLLink href="https://www.youtube.com/watch?v=sJXn4Cl4oww"> podcast </UDLLink> discussing book.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>12/19/23</HeroNewsItemDate>
|
<HeroNewsItemDate>12/19/23</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent>Deeper Insights <a href="https://podcasts.apple.com/us/podcast/understanding-deep-learning-with-simon-prince/id1669436318?i=1000638269385">podcast</a> discussing book.</HeroNewsItemContent>
|
<HeroNewsItemContent>Deeper Insights <UDLLink href="https://podcasts.apple.com/us/podcast/understanding-deep-learning-with-simon-prince/id1669436318?i=1000638269385">podcast</UDLLink> discussing book.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>12/06/23</HeroNewsItemDate>
|
<HeroNewsItemDate>12/06/23</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent> I did an <a href="https://www.borealisai.com/news/understanding-deep-learning/">interview</a> discussing the book with Borealis AI.</HeroNewsItemContent>
|
<HeroNewsItemContent> I did an <UDLLink href="https://www.borealisai.com/news/understanding-deep-learning/">interview</UDLLink> discussing the book with Borealis AI.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
|
|
||||||
<HeroNewsItem>
|
<HeroNewsItem>
|
||||||
<HeroNewsItemDate>12/05/23</HeroNewsItemDate>
|
<HeroNewsItemDate>12/05/23</HeroNewsItemDate>
|
||||||
<HeroNewsItemContent> Book released by <a href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">The MIT Press</a>.</HeroNewsItemContent>
|
<HeroNewsItemContent> Book released by <UDLLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">The MIT Press</UDLLink>.</HeroNewsItemContent>
|
||||||
</HeroNewsItem>
|
</HeroNewsItem>
|
||||||
</HeroNewsBlock>
|
</HeroNewsBlock>
|
||||||
<HeroFollowBlock>
|
|
||||||
Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a
|
|
||||||
href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
|
|
||||||
</HeroFollowBlock>
|
|
||||||
<HeroCitationTitle>CITATION:</HeroCitationTitle>
|
<HeroCitationTitle>CITATION:</HeroCitationTitle>
|
||||||
<HeroCitationBlock>
|
<HeroCitationBlock>
|
||||||
<pre>
|
<pre>
|
||||||
@@ -71,16 +70,20 @@ const HeroSection = () => {
|
|||||||
</code>
|
</code>
|
||||||
</pre>
|
</pre>
|
||||||
</HeroCitationBlock>
|
</HeroCitationBlock>
|
||||||
|
<HeroFollowBlock>
|
||||||
|
Follow me on <UDLLink href="https://twitter.com/SimonPrinceAI">Twitter</UDLLink> or <UDLLink
|
||||||
|
href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</UDLLink> for updates.
|
||||||
|
</HeroFollowBlock>
|
||||||
</HeroColumn1>
|
</HeroColumn1>
|
||||||
<HeroColumn2>
|
<HeroColumn2>
|
||||||
<HeroImgWrap>
|
<HeroImgWrap>
|
||||||
<Img src={img} alt="book cover"/>
|
<Img src={img} alt="book cover"/>
|
||||||
</HeroImgWrap>
|
</HeroImgWrap>
|
||||||
<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v2.03/UnderstandingDeepLearning_02_26_24_C.pdf">Download full pdf</HeroLink>
|
<HeroLink href="https://github.com/udlbook/udlbook/releases/download/v2.05/UnderstandingDeepLearning_04_18_24_C.pdf">Download full pdf (18 Apr 2024)</HeroLink>
|
||||||
<HeroDownloadsImg src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield"/>
|
<HeroDownloadsImg src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield"/>
|
||||||
<HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">Buy the book</HeroLink>
|
<HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">Buy the book</HeroLink>
|
||||||
<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">Answers to selected questions</HeroLink>
|
<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">Answers to selected questions</HeroLink>
|
||||||
<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">Find/Report Errata</HeroLink>
|
<HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">Errata</HeroLink>
|
||||||
</HeroColumn2>
|
</HeroColumn2>
|
||||||
</HeroRow>
|
</HeroRow>
|
||||||
</HeroContent>
|
</HeroContent>
|
||||||
|
|||||||
@@ -127,4 +127,39 @@ export const InstructorsContent = styled.div`
|
|||||||
flex-direction: column;
|
flex-direction: column;
|
||||||
align-items: left ;
|
align-items: left ;
|
||||||
list-style-position: inside;
|
list-style-position: inside;
|
||||||
|
@media screen and (max-width: 1050px) {
|
||||||
|
font-size: 12px;
|
||||||
|
}
|
||||||
|
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 10px;
|
||||||
|
}
|
||||||
|
`
|
||||||
|
|
||||||
|
export const InstructorsLink = styled.a`
|
||||||
|
text-decoration: none;
|
||||||
|
color: #555;
|
||||||
|
font-weight: 300;
|
||||||
|
margin: 0 2px;
|
||||||
|
position: relative;
|
||||||
|
|
||||||
|
&:before{
|
||||||
|
position: absolute;
|
||||||
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #555;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
|
&:hover:before {
|
||||||
|
opacity: 1;
|
||||||
|
-webkit-transform: scaleX(1.05);
|
||||||
|
}
|
||||||
`
|
`
|
||||||
@@ -1,5 +1,5 @@
|
|||||||
import React from 'react'
|
import React from 'react'
|
||||||
import { ImgWrap, Img, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'
|
import { ImgWrap, Img, InstructorsLink, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'
|
||||||
|
|
||||||
// export const homeObjOne = {
|
// export const homeObjOne = {
|
||||||
// id: 'about',
|
// id: 'about',
|
||||||
@@ -45,7 +45,7 @@ const InstructorsSection = () => {
|
|||||||
<InstructorsRow2>
|
<InstructorsRow2>
|
||||||
<Column1>
|
<Column1>
|
||||||
<TopLine>Register</TopLine>
|
<TopLine>Register</TopLine>
|
||||||
<a href="https://mitpress.ublish.com/request?cri=15055">Register</a> with MIT Press for answer booklet.
|
<InstructorsLink href="https://mitpress.ublish.com/request?cri=15055">Register</InstructorsLink> with MIT Press for answer booklet.
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
|
|
||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
@@ -56,19 +56,19 @@ const InstructorsSection = () => {
|
|||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
<ol>
|
<ol>
|
||||||
<li>Introduction <a href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">PPTX</a></li>
|
<li>Introduction <InstructorsLink href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">PPTX</InstructorsLink></li>
|
||||||
<li>Supervised Learning <a href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">PPTX</a></li>
|
<li>Supervised Learning <InstructorsLink href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">PPTX</InstructorsLink></li>
|
||||||
<li>Shallow Neural Networks <a href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">PPTX</a></li>
|
<li>Shallow Neural Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">PPTX</InstructorsLink></li>
|
||||||
<li>Deep Neural Networks <a href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">PPTX</a></li>
|
<li>Deep Neural Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">PPTX</InstructorsLink></li>
|
||||||
<li>Loss Functions <a href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">PPTX</a></li>
|
<li>Loss Functions <InstructorsLink href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">PPTX</InstructorsLink></li>
|
||||||
<li>Fitting Models <a href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">PPTX</a></li>
|
<li>Fitting Models <InstructorsLink href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">PPTX</InstructorsLink></li>
|
||||||
<li>Computing Gradients <a href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">PPTX</a></li>
|
<li>Computing Gradients <InstructorsLink href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">PPTX</InstructorsLink></li>
|
||||||
<li>Initialization <a href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">PPTX</a></li>
|
<li>Initialization <InstructorsLink href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">PPTX</InstructorsLink></li>
|
||||||
<li>Performance <a href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">PPTX</a></li>
|
<li>Performance <InstructorsLink href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">PPTX</InstructorsLink></li>
|
||||||
<li>Regularization <a href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">PPTX</a></li>
|
<li>Regularization <InstructorsLink href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">PPTX</InstructorsLink></li>
|
||||||
<li>Convolutional Networks <a href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">PPTX</a></li>
|
<li>Convolutional Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">PPTX</InstructorsLink></li>
|
||||||
<li>Image Generation <a href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">PPTX</a></li>
|
<li>Image Generation <InstructorsLink href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">PPTX</InstructorsLink></li>
|
||||||
<li>Transformers and LLMs <a href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">PPTX</a></li>
|
<li>Transformers and LLMs <InstructorsLink href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">PPTX</InstructorsLink></li>
|
||||||
</ol>
|
</ol>
|
||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
</Column1>
|
</Column1>
|
||||||
@@ -76,92 +76,92 @@ const InstructorsSection = () => {
|
|||||||
<TopLine>Figures</TopLine>
|
<TopLine>Figures</TopLine>
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
<ol>
|
<ol>
|
||||||
<li> Introduction: <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv"> SVG</a> / <a href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX </a></li>
|
<li> Introduction: <InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX </InstructorsLink></li>
|
||||||
|
|
||||||
<li> Supervised learning: <a
|
<li> Supervised learning: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG</a> / <a href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Shallow neural networks: <a
|
<li> Shallow neural networks: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG</a> / <a href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Deep neural networks: <a
|
<li> Deep neural networks: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG</a>
|
href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG</InstructorsLink>
|
||||||
/
|
/
|
||||||
<a href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
<InstructorsLink href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Loss functions: <a
|
<li> Loss functions: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
|
||||||
</a> / <a href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
|
</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
|
||||||
SVG
|
SVG
|
||||||
</a> / <a
|
</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Training models: <a
|
<li> Training models: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF
|
||||||
</a> / <a href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
|
</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
|
||||||
SVG
|
SVG
|
||||||
</a> / <a
|
</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Gradients and initialization: <a
|
<li> Gradients and initialization: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG</a> / <a href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Measuring performance: <a
|
<li> Measuring performance: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG</a> / <a href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Regularization: <a
|
<li> Regularization: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF
|
||||||
</a> / <a href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
|
</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
|
||||||
SVG
|
SVG
|
||||||
</a> / <a
|
</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Convolutional networks: <a
|
<li> Convolutional networks: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG</a> / <a href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Residual networks: <a
|
<li> Residual networks: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG</a> / <a href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Transformers: <a
|
<li> Transformers: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
|
||||||
SVG</a> / <a
|
SVG</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Graph neural networks: <a
|
<li> Graph neural networks: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG</a> / <a href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Unsupervised learning: <a
|
<li> Unsupervised learning: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG</a> / <a href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
||||||
PPTX</a></li>
|
PPTX</InstructorsLink></li>
|
||||||
<li> GANs: <a
|
<li> GANs: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG</a> / <a href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Normalizing flows: <a
|
<li> Normalizing flows: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG</a> / <a href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Variational autoencoders: <a
|
<li> Variational autoencoders: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG</a> / <a href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Diffusion models: <a
|
<li> Diffusion models: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG</a> /
|
href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG</InstructorsLink> /
|
||||||
<a href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
<InstructorsLink href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
||||||
PPTX</a></li>
|
PPTX</InstructorsLink></li>
|
||||||
<li> Deep reinforcement learning: <a
|
<li> Deep reinforcement learning: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG</a> / <a href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
||||||
PPTX </a></li>
|
PPTX </InstructorsLink></li>
|
||||||
<li> Why does deep learning work?: <a
|
<li> Why does deep learning work?: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG</a> / <a href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
|
||||||
PPTX</a></li>
|
PPTX</InstructorsLink></li>
|
||||||
<li> Deep learning and ethics: <a
|
<li> Deep learning and ethics: <InstructorsLink
|
||||||
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF</a> / <a
|
href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF</InstructorsLink> / <InstructorsLink
|
||||||
href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU">SVG</a> / <a
|
href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU">SVG</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
<li> Appendices - <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
|
<li> Appendices - <InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
|
||||||
SVG</a> / <a
|
SVG</InstructorsLink> / <InstructorsLink
|
||||||
href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
|
href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
|
||||||
</ol>
|
</ol>
|
||||||
</InstructorsContent>
|
</InstructorsContent>
|
||||||
<a href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">Instructions</a> for editing equations in figures.
|
<InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">Instructions</InstructorsLink> for editing equations in figures.
|
||||||
|
|
||||||
<InstructorsContent>
|
<InstructorsContent>
|
||||||
|
|
||||||
|
|||||||
@@ -125,6 +125,10 @@ export const MediaContent = styled.div`
|
|||||||
flex-direction: column;
|
flex-direction: column;
|
||||||
align-items: left ;
|
align-items: left ;
|
||||||
list-style-position: inside;
|
list-style-position: inside;
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 14px;
|
||||||
|
}
|
||||||
|
|
||||||
`
|
`
|
||||||
|
|
||||||
export const MediaRow2 = styled.div`
|
export const MediaRow2 = styled.div`
|
||||||
@@ -136,4 +140,44 @@ export const MediaRow2 = styled.div`
|
|||||||
@media screen and (max-width: 768px){
|
@media screen and (max-width: 768px){
|
||||||
grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
|
grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
|
||||||
}
|
}
|
||||||
|
`
|
||||||
|
|
||||||
|
export const VideoFrame=styled.div`
|
||||||
|
width: 560px ;
|
||||||
|
height: 315px ;
|
||||||
|
@media screen and (max-width: 1050px) {
|
||||||
|
width: 280px ;
|
||||||
|
height: 157px ;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
`
|
||||||
|
|
||||||
|
|
||||||
|
export const MediaLink = styled.a`
|
||||||
|
text-decoration: none;
|
||||||
|
color: #57c6d1;
|
||||||
|
font-weight: 300;
|
||||||
|
margin: 0 2px;
|
||||||
|
position: relative;
|
||||||
|
|
||||||
|
&:before{
|
||||||
|
position: absolute;
|
||||||
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #57c6d1;;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
|
&:hover:before {
|
||||||
|
opacity: 1;
|
||||||
|
-webkit-transform: scaleX(1.05);
|
||||||
|
}
|
||||||
`
|
`
|
||||||
@@ -1,5 +1,5 @@
|
|||||||
import React from 'react'
|
import React from 'react'
|
||||||
import { ImgWrap, Img, MediaContainer, MediaContent, MediaWrapper, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'
|
import { ImgWrap, Img, MediaLink, MediaContainer, MediaContent, MediaWrapper, VideoFrame, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'
|
||||||
|
|
||||||
// export const homeObjOne = {
|
// export const homeObjOne = {
|
||||||
// id: 'about',
|
// id: 'about',
|
||||||
@@ -45,11 +45,18 @@ const MediaSection = () => {
|
|||||||
<MediaRow>
|
<MediaRow>
|
||||||
<Column1>
|
<Column1>
|
||||||
Machine learning street talk podcast
|
Machine learning street talk podcast
|
||||||
<iframe width="560" height="315" src="https://www.youtube.com/embed/sJXn4Cl4oww?si=Lm_hQPqj0RXy-75H&controls=0" title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
|
<VideoFrame>
|
||||||
|
<iframe width="100%" height="100%"
|
||||||
|
src="https://www.youtube.com/embed/sJXn4Cl4oww?si=Lm_hQPqj0RXy-75H&controls=0"
|
||||||
|
title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen>
|
||||||
|
</iframe>
|
||||||
|
</VideoFrame>
|
||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
Deeper insights podcast
|
Deeper insights podcast
|
||||||
<iframe width="560" height="315" src="https://www.youtube.com/embed/nQf4o9TDSHI?si=uMk66zLD7uhuSnQ1&controls=0" title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
|
<VideoFrame>
|
||||||
|
<iframe width="100%" height="100%" src="https://www.youtube.com/embed/nQf4o9TDSHI?si=uMk66zLD7uhuSnQ1&controls=0" title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
|
||||||
|
</VideoFrame>
|
||||||
</Column2>
|
</Column2>
|
||||||
</MediaRow>
|
</MediaRow>
|
||||||
<MediaRow2>
|
<MediaRow2>
|
||||||
@@ -57,9 +64,9 @@ const MediaSection = () => {
|
|||||||
<TopLine>Reviews</TopLine>
|
<TopLine>Reviews</TopLine>
|
||||||
<MediaContent>
|
<MediaContent>
|
||||||
<ul>
|
<ul>
|
||||||
<li> Amazon <a href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">reviews</a></li>
|
<li> Amazon <MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">reviews</MediaLink></li>
|
||||||
<li>Goodreads <a href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">reviews </a></li>
|
<li>Goodreads <MediaLink href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">reviews </MediaLink></li>
|
||||||
<li>Book <a href="https://medium.com/@vishalvignesh/udl-book-review-the-new-deep-learning-textbook-youll-want-to-finish-69e1557b018d">review</a> by Vishal V.</li>
|
<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>
|
||||||
</ul>
|
</ul>
|
||||||
</MediaContent>
|
</MediaContent>
|
||||||
</Column1>
|
</Column1>
|
||||||
@@ -67,8 +74,8 @@ const MediaSection = () => {
|
|||||||
<TopLine>Interviews</TopLine>
|
<TopLine>Interviews</TopLine>
|
||||||
<MediaContent>
|
<MediaContent>
|
||||||
<ul>
|
<ul>
|
||||||
<li>Borealis AI <a href="https://www.borealisai.com/news/understanding-deep-learning/">interview</a></li>
|
<li>Borealis AI <MediaLink href="https://www.borealisai.com/news/understanding-deep-learning/">interview</MediaLink></li>
|
||||||
<li>Shepherd ML book <a href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">recommendations</a></li>
|
<li>Shepherd ML book <MediaLink href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">recommendations</MediaLink></li>
|
||||||
</ul>
|
</ul>
|
||||||
</MediaContent>
|
</MediaContent>
|
||||||
</Column2>
|
</Column2>
|
||||||
|
|||||||
@@ -135,10 +135,18 @@ export const MoreOuterList = styled.ul`
|
|||||||
// list-style:none;
|
// list-style:none;
|
||||||
list-style-position: inside;
|
list-style-position: inside;
|
||||||
margin:0;
|
margin:0;
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 14px;
|
||||||
|
}
|
||||||
|
|
||||||
`
|
`
|
||||||
|
|
||||||
export const MoreInnerList = styled.ul`
|
export const MoreInnerList = styled.ul`
|
||||||
list-style-position: inside;
|
list-style-position: inside;
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 12px;
|
||||||
|
}
|
||||||
|
|
||||||
`
|
`
|
||||||
|
|
||||||
export const MoreInnerP = styled.p`
|
export const MoreInnerP = styled.p`
|
||||||
@@ -149,19 +157,31 @@ export const MoreInnerP = styled.p`
|
|||||||
color: #fff
|
color: #fff
|
||||||
`
|
`
|
||||||
|
|
||||||
|
|
||||||
export const MoreLink = styled.a`
|
export const MoreLink = styled.a`
|
||||||
color: #fff;
|
text-decoration: none;
|
||||||
text-decoration: none;
|
color: #555;
|
||||||
padding: 0.1rem 0rem;
|
font-weight: 300;
|
||||||
height: 100%;
|
margin: 0 2px;
|
||||||
cursor: pointer;
|
position: relative;
|
||||||
|
|
||||||
&:hover {
|
&:before{
|
||||||
filter: brightness(0.85);
|
position: absolute;
|
||||||
}
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #555;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
&.active {
|
&:hover:before {
|
||||||
color: #000
|
opacity: 1;
|
||||||
border-bottom: 3px solid #01bf71;
|
-webkit-transform: scaleX(1.05);
|
||||||
}
|
}
|
||||||
`;
|
`
|
||||||
@@ -1,5 +1,5 @@
|
|||||||
import React from 'react'
|
import React from 'react'
|
||||||
import { ImgWrap, Img, MoreContainer, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
|
import { ImgWrap, Img, MoreContainer, MoreLink, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
|
||||||
import img from '../../images/more.svg'
|
import img from '../../images/more.svg'
|
||||||
|
|
||||||
|
|
||||||
@@ -28,7 +28,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Book</TopLine>
|
<TopLine>Book</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</a>
|
<MoreLink href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> 2012 book published with CUP </li>
|
<li> 2012 book published with CUP </li>
|
||||||
@@ -44,7 +44,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Transformers & LLMs</TopLine>
|
<TopLine>Transformers & LLMs</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/" target="_blank" rel="noreferrer">Intro to LLMs</a>
|
<MoreLink href="https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/" target="_blank" rel="noreferrer">Intro to LLMs</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> What is an LLM?</li>
|
<li> What is an LLM?</li>
|
||||||
@@ -57,7 +57,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank" rel="noreferrer">Transformers I</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank" rel="noreferrer">Transformers I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Dot-Product self-attention </li>
|
<li> Dot-Product self-attention </li>
|
||||||
@@ -72,7 +72,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank" rel="noreferrer">Transformers II</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank" rel="noreferrer">Transformers II</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Sinusoidal position embeddings </li>
|
<li> Sinusoidal position embeddings </li>
|
||||||
@@ -93,7 +93,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank" rel="noreferrer">Transformers III</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank" rel="noreferrer">Transformers III</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Tricks for training transformers </li>
|
<li> Tricks for training transformers </li>
|
||||||
@@ -106,7 +106,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/" target="_blank" rel="noreferrer">Training and fine-tuning LLMs</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Large language models </li>
|
<li> Large language models </li>
|
||||||
@@ -118,7 +118,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/" target="_blank" rel="noreferrer">Speeding up inference in LLMs</a>
|
<MoreLink href="https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/" target="_blank" rel="noreferrer">Speeding up inference in LLMs</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Problems with transformers</li>
|
<li> Problems with transformers</li>
|
||||||
@@ -135,7 +135,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Math for machine learning</TopLine>
|
<TopLine>Math for machine learning</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing" target="_blank" rel="noreferrer">Linear algebra</a>
|
<MoreLink href="https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing" target="_blank" rel="noreferrer">Linear algebra</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Vectors and matrices </li>
|
<li> Vectors and matrices </li>
|
||||||
@@ -154,7 +154,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing" target="_blank" rel="noreferrer">Introduction to probability</a>
|
<MoreLink href="https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing" target="_blank" rel="noreferrer">Introduction to probability</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Random variables </li>
|
<li> Random variables </li>
|
||||||
@@ -168,7 +168,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank" rel="noreferrer">Probability distributions</a>
|
<MoreLink href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank" rel="noreferrer">Probability distributions</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Bernouilli distribution </li>
|
<li> Bernouilli distribution </li>
|
||||||
@@ -184,7 +184,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing" target="_blank" rel="noreferrer">Fitting probability distributions</a>
|
<MoreLink href="https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing" target="_blank" rel="noreferrer">Fitting probability distributions</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Maximum likelihood </li>
|
<li> Maximum likelihood </li>
|
||||||
@@ -196,7 +196,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank" rel="noreferrer">The normal distribution</a>
|
<MoreLink href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank" rel="noreferrer">The normal distribution</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Types of covariance matrix </li>
|
<li> Types of covariance matrix </li>
|
||||||
@@ -214,7 +214,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Optimization</TopLine>
|
<TopLine>Optimization</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing" target="_blank" rel="noreferrer">Gradient-based optimmization</a>
|
<MoreLink href="https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing" target="_blank" rel="noreferrer">Gradient-based optimization</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Convexity </li>
|
<li> Convexity </li>
|
||||||
@@ -227,7 +227,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank" rel="noreferrer">Bayesian optimization</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank" rel="noreferrer">Bayesian optimization</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Gaussian processes </li>
|
<li> Gaussian processes </li>
|
||||||
@@ -243,7 +243,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/" target="_blank" rel="noreferrer">SAT Solvers I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Boolean logic and satisfiability </li>
|
<li> Boolean logic and satisfiability </li>
|
||||||
@@ -258,7 +258,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/" target="_blank" rel="noreferrer">SAT Solvers II</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/" target="_blank" rel="noreferrer">SAT Solvers II</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Conditioning </li>
|
<li> Conditioning </li>
|
||||||
@@ -272,7 +272,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank" rel="noreferrer">SAT Solvers III</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Satisfiability vs. problem size </li>
|
<li> Satisfiability vs. problem size </li>
|
||||||
@@ -286,7 +286,7 @@ const MoreSection = () => {
|
|||||||
</li>
|
</li>
|
||||||
</MoreOuterList>
|
</MoreOuterList>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank" rel="noreferrer">SAT Solvers III</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Satisfiability vs. problem size </li>
|
<li> Satisfiability vs. problem size </li>
|
||||||
@@ -302,7 +302,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Computer vision</TopLine>
|
<TopLine>Computer vision</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</a>
|
<MoreLink href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Whitening </li>
|
<li> Whitening </li>
|
||||||
@@ -314,7 +314,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</a>
|
<MoreLink href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Pinhole camera model </li>
|
<li> Pinhole camera model </li>
|
||||||
@@ -327,7 +327,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</a>
|
<MoreLink href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Euclidean, similarity, affine, projective transformations </li>
|
<li> Euclidean, similarity, affine, projective transformations </li>
|
||||||
@@ -340,7 +340,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</a>
|
<MoreLink href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Two view geometry </li>
|
<li> Two view geometry </li>
|
||||||
@@ -357,7 +357,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Reinforcement learning</TopLine>
|
<TopLine>Reinforcement learning</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://arxiv.org/abs/2307.05979" target="_blank" rel="noreferrer">Transformers in RL</a>
|
<MoreLink href="https://arxiv.org/abs/2307.05979" target="_blank" rel="noreferrer">Transformers in RL</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Challenges in RL</li>
|
<li> Challenges in RL</li>
|
||||||
@@ -381,7 +381,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>AI Theory</TopLine>
|
<TopLine>AI Theory</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank" rel="noreferrer">Gradient flow</a>
|
<MoreLink href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank" rel="noreferrer">Gradient flow</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Gradient flow </li>
|
<li> Gradient flow </li>
|
||||||
@@ -393,7 +393,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank" rel="noreferrer">Neural tangent kernel</a>
|
<MoreLink href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank" rel="noreferrer">Neural tangent kernel</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Infinite width neural networks </li>
|
<li> Infinite width neural networks </li>
|
||||||
@@ -410,7 +410,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Temporal models</TopLine>
|
<TopLine>Temporal models</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank" rel="noreferrer">Temporal models</a>
|
<MoreLink href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank" rel="noreferrer">Temporal models</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Kalman filter </li>
|
<li> Kalman filter </li>
|
||||||
@@ -426,7 +426,7 @@ const MoreSection = () => {
|
|||||||
<TopLine> Unsupervised learning</TopLine>
|
<TopLine> Unsupervised learning</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing" target="_blank" rel="noreferrer">Modeling complex data densities</a>
|
<MoreLink href="https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing" target="_blank" rel="noreferrer">Modeling complex data densities</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Hidden variables </li>
|
<li> Hidden variables </li>
|
||||||
@@ -440,7 +440,7 @@ const MoreSection = () => {
|
|||||||
</li>
|
</li>
|
||||||
|
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank" rel="noreferrer">Variational autoencoders</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank" rel="noreferrer">Variational autoencoders</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Non-linear latent variable models </li>
|
<li> Non-linear latent variable models </li>
|
||||||
@@ -453,7 +453,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://arxiv.org/abs/1908.09257" target="_blank" rel="noreferrer">Normalizing flows: introduction and review</a>
|
<MoreLink href="https://arxiv.org/abs/1908.09257" target="_blank" rel="noreferrer">Normalizing flows: introduction and review</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Normalizing flows </li>
|
<li> Normalizing flows </li>
|
||||||
@@ -471,7 +471,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Graphical Models</TopLine>
|
<TopLine>Graphical Models</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing" target="_blank" rel="noreferrer">Graphical models</a>
|
<MoreLink href="https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing" target="_blank" rel="noreferrer">Graphical models</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Conditional independence </li>
|
<li> Conditional independence </li>
|
||||||
@@ -484,7 +484,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing" target="_blank" rel="noreferrer">Models for chains and trees</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Hidden Markov models </li>
|
<li> Hidden Markov models </li>
|
||||||
@@ -498,7 +498,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank" rel="noreferrer">Models for grids</a>
|
<MoreLink href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank" rel="noreferrer">Models for grids</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Markov random fields </li>
|
<li> Markov random fields </li>
|
||||||
@@ -515,7 +515,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Machine learning</TopLine>
|
<TopLine>Machine learning</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing" target="_blank" rel="noreferrer">Learning and inference</a>
|
<MoreLink href="https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing" target="_blank" rel="noreferrer">Learning and inference</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Discriminative models </li>
|
<li> Discriminative models </li>
|
||||||
@@ -526,7 +526,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank" rel="noreferrer">Regression models</a>
|
<MoreLink href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank" rel="noreferrer">Regression models</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Linear regression </li>
|
<li> Linear regression </li>
|
||||||
@@ -541,7 +541,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank" rel="noreferrer">Classification models</a>
|
<MoreLink href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank" rel="noreferrer">Classification models</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Logistic regression </li>
|
<li> Logistic regression </li>
|
||||||
@@ -555,7 +555,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink 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</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Meta-learning framework </li>
|
<li> Meta-learning framework </li>
|
||||||
@@ -567,7 +567,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink 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</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> MAML & Reptile </li>
|
<li> MAML & Reptile </li>
|
||||||
@@ -585,7 +585,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Natural language processing</TopLine>
|
<TopLine>Natural language processing</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/" target="_blank" rel="noreferrer">Neural natural language generation I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Encoder-decoder architecture </li>
|
<li> Encoder-decoder architecture </li>
|
||||||
@@ -599,7 +599,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink 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</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Fine-tuning with reinforcement learning </li>
|
<li> Fine-tuning with reinforcement learning </li>
|
||||||
@@ -614,7 +614,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/" target="_blank" rel="noreferrer">Parsing I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Parse trees </li>
|
<li> Parse trees </li>
|
||||||
@@ -626,7 +626,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/" target="_blank" rel="noreferrer">Parsing II</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Weighted context-free grammars </li>
|
<li> Weighted context-free grammars </li>
|
||||||
@@ -638,7 +638,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/" target="_blank" rel="noreferrer">Parsing III</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Probabilistic context-free grammars </li>
|
<li> Probabilistic context-free grammars </li>
|
||||||
@@ -652,7 +652,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank" rel="noreferrer">XLNet</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank" rel="noreferrer">XLNet</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Language modeling </li>
|
<li> Language modeling </li>
|
||||||
@@ -670,7 +670,7 @@ const MoreSection = () => {
|
|||||||
<TopLine>Responsible AI</TopLine>
|
<TopLine>Responsible AI</TopLine>
|
||||||
<MoreOuterList>
|
<MoreOuterList>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/" target="_blank" rel="noreferrer">Bias and fairness</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/" target="_blank" rel="noreferrer">Bias and fairness</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Sources of bias</li>
|
<li> Sources of bias</li>
|
||||||
@@ -683,7 +683,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank" rel="noreferrer">Explainability I</a>
|
<MoreLink href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank" rel="noreferrer">Explainability I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Taxonomy of XAI approaches</li>
|
<li> Taxonomy of XAI approaches</li>
|
||||||
@@ -696,7 +696,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/" target="_blank" rel="noreferrer">Explainability II</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Global feature importance</li>
|
<li> Global feature importance</li>
|
||||||
@@ -710,7 +710,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<li>
|
<li>
|
||||||
<a href="https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/" target="_blank" rel="noreferrer">Differential privacy I</a>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/" target="_blank" rel="noreferrer">Differential privacy I</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Early approaches to privacy </li>
|
<li> Early approaches to privacy </li>
|
||||||
@@ -724,7 +724,7 @@ const MoreSection = () => {
|
|||||||
</MoreInnerP>
|
</MoreInnerP>
|
||||||
</li>
|
</li>
|
||||||
<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>
|
<MoreLink href="https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/" target="_blank" rel="noreferrer">Differential privacy II</MoreLink>
|
||||||
<MoreInnerP>
|
<MoreInnerP>
|
||||||
<MoreInnerList>
|
<MoreInnerList>
|
||||||
<li> Differential privacy and matchine learning</li>
|
<li> Differential privacy and matchine learning</li>
|
||||||
|
|||||||
@@ -39,6 +39,10 @@ export const NavLogo = styled(LinkR)`
|
|||||||
margin-left: 24px;
|
margin-left: 24px;
|
||||||
font-weight: bold;
|
font-weight: bold;
|
||||||
text-decoration: none;
|
text-decoration: none;
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 1.0rem;
|
||||||
|
}
|
||||||
|
|
||||||
`;
|
`;
|
||||||
|
|
||||||
export const MobileIcon = styled.div`
|
export const MobileIcon = styled.div`
|
||||||
|
|||||||
@@ -29,7 +29,7 @@ const Navbar = ( {toggle} ) => {
|
|||||||
<IconContext.Provider value={{color: '#fff'}}>
|
<IconContext.Provider value={{color: '#fff'}}>
|
||||||
<Nav scrollNav={scrollNav}>
|
<Nav scrollNav={scrollNav}>
|
||||||
<NavbarContainer>
|
<NavbarContainer>
|
||||||
<NavLogo to="/" onClick={toggleHome}>
|
<NavLogo to="/udlbook/" onClick={toggleHome}>
|
||||||
<h1> Understanding Deep Learning </h1>
|
<h1> Understanding Deep Learning </h1>
|
||||||
</NavLogo>
|
</NavLogo>
|
||||||
<MobileIcon onClick={toggle}>
|
<MobileIcon onClick={toggle}>
|
||||||
|
|||||||
@@ -34,16 +34,30 @@ export const NotebookRow = styled.div`
|
|||||||
}
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
export const Column1 = styled.div`
|
export const Column1 = styled.p`
|
||||||
margin-bottom: 15px;
|
margin-bottom: 15px;
|
||||||
padding: 0 15px;
|
padding: 0 15px;
|
||||||
grid-area: col1;
|
grid-area: col1;
|
||||||
|
@media screen and (max-width: 1050px) {
|
||||||
|
font-size: 12px;
|
||||||
|
}
|
||||||
|
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 10px;
|
||||||
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
export const Column2 = styled.div`
|
export const Column2 = styled.p`
|
||||||
margin-bottom: 15px;
|
margin-bottom: 15px;
|
||||||
padding: 0 15px;
|
padding: 0 15px;
|
||||||
grid-area: col2;
|
grid-area: col2;
|
||||||
|
@media screen and (max-width: 1050px) {
|
||||||
|
font-size: 12px;
|
||||||
|
}
|
||||||
|
|
||||||
|
@media screen and (max-width: 768px) {
|
||||||
|
font-size: 10px;
|
||||||
|
}
|
||||||
`
|
`
|
||||||
|
|
||||||
export const TextWrapper = styled.div`
|
export const TextWrapper = styled.div`
|
||||||
@@ -83,7 +97,6 @@ export const Subtitle = styled.p`
|
|||||||
font-size: 18px;
|
font-size: 18px;
|
||||||
line-height: 24px;
|
line-height: 24px;
|
||||||
color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
|
color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
|
||||||
|
|
||||||
`
|
`
|
||||||
|
|
||||||
export const BtnWrap = styled.div`
|
export const BtnWrap = styled.div`
|
||||||
@@ -103,3 +116,32 @@ export const Img = styled.img`
|
|||||||
margin-left: 10px;
|
margin-left: 10px;
|
||||||
padding-right: 0;
|
padding-right: 0;
|
||||||
`;
|
`;
|
||||||
|
|
||||||
|
|
||||||
|
export const NBLink = styled.a`
|
||||||
|
text-decoration: none;
|
||||||
|
color: #57c6d1;;
|
||||||
|
font-weight: 300;
|
||||||
|
margin: 0 2px;
|
||||||
|
position: relative;
|
||||||
|
|
||||||
|
&:before{
|
||||||
|
position: absolute;
|
||||||
|
margin: 0 auto;
|
||||||
|
top: 100%;
|
||||||
|
left: 0;
|
||||||
|
width: 100%;
|
||||||
|
height: 2px;
|
||||||
|
background-color: #57c6d1;;
|
||||||
|
content: '';
|
||||||
|
opacity: .3;
|
||||||
|
-webkit-transform: scaleX(1);
|
||||||
|
transition-property: opacity, -webkit-transform;
|
||||||
|
transition-duration: .3s;
|
||||||
|
}
|
||||||
|
|
||||||
|
&:hover:before {
|
||||||
|
opacity: 1;
|
||||||
|
-webkit-transform: scaleX(1.05);
|
||||||
|
}
|
||||||
|
`
|
||||||
@@ -1,5 +1,5 @@
|
|||||||
import React from 'react'
|
import React from 'react'
|
||||||
import { ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements'
|
import { NBLink, ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements'
|
||||||
|
|
||||||
// export const homeObjOne = {
|
// export const homeObjOne = {
|
||||||
// id: 'about',
|
// id: 'about',
|
||||||
@@ -45,168 +45,168 @@ const NotebookSection = () => {
|
|||||||
<NotebookRow>
|
<NotebookRow>
|
||||||
<Column1>
|
<Column1>
|
||||||
<ul>
|
<ul>
|
||||||
<li> Notebook 1.1 - Background mathematics: <a
|
<li> Notebook 1.1 - Background mathematics: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 2.1 - Supervised learning: <a
|
<li> Notebook 2.1 - Supervised learning: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 3.1 - Shallow networks I: <a
|
<li> Notebook 3.1 - Shallow networks I: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 3.2 - Shallow networks II: <a
|
<li> Notebook 3.2 - Shallow networks II: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 3.3 - Shallow network regions: <a
|
<li> Notebook 3.3 - Shallow network regions: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 3.4 - Activation functions: <a
|
<li> Notebook 3.4 - Activation functions: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 4.1 - Composing networks: <a
|
<li> Notebook 4.1 - Composing networks: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 4.2 - Clipping functions: <a
|
<li> Notebook 4.2 - Clipping functions: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 4.3 - Deep networks: <a
|
<li> Notebook 4.3 - Deep networks: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 5.1 - Least squares loss: <a
|
<li> Notebook 5.1 - Least squares loss: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 5.2 - Binary cross-entropy loss: <a
|
<li> Notebook 5.2 - Binary cross-entropy loss: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 5.3 - Multiclass cross-entropy loss: <a
|
<li> Notebook 5.3 - Multiclass cross-entropy loss: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 6.1 - Line search: <a
|
<li> Notebook 6.1 - Line search: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 6.2 - Gradient descent: <a
|
<li> Notebook 6.2 - Gradient descent: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 6.3 - Stochastic gradient descent: <a
|
<li> Notebook 6.3 - Stochastic gradient descent: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 6.4 - Momentum: <a
|
<li> Notebook 6.4 - Momentum: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 6.5 - Adam: <a
|
<li> Notebook 6.5 - Adam: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 7.1 - Backpropagation in toy model: <a
|
<li> Notebook 7.1 - Backpropagation in toy model: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 7.2 - Backpropagation: <a
|
<li> Notebook 7.2 - Backpropagation: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 7.3 - Initialization: <a
|
<li> Notebook 7.3 - Initialization: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 8.1 - MNIST-1D performance: <a
|
<li> Notebook 8.1 - MNIST-1D performance: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 8.2 - Bias-variance trade-off: <a
|
<li> Notebook 8.2 - Bias-variance trade-off: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 8.3 - Double descent: <a
|
<li> Notebook 8.3 - Double descent: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 8.4 - High-dimensional spaces: <a
|
<li> Notebook 8.4 - High-dimensional spaces: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 9.1 - L2 regularization: <a
|
<li> Notebook 9.1 - L2 regularization: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 9.2 - Implicit regularization: <a
|
<li> Notebook 9.2 - Implicit regularization: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 9.3 - Ensembling: <a
|
<li> Notebook 9.3 - Ensembling: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 9.4 - Bayesian approach: <a
|
<li> Notebook 9.4 - Bayesian approach: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 9.5 - Augmentation <a
|
<li> Notebook 9.5 - Augmentation <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 10.1 - 1D convolution: <a
|
<li> Notebook 10.1 - 1D convolution: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 10.2 - Convolution for MNIST-1D: <a
|
<li> Notebook 10.2 - Convolution for MNIST-1D: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 10.3 - 2D convolution: <a
|
<li> Notebook 10.3 - 2D convolution: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 10.4 - Downsampling & upsampling: <a
|
<li> Notebook 10.4 - Downsampling & upsampling: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 10.5 - Convolution for MNIST: <a
|
<li> Notebook 10.5 - Convolution for MNIST: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
</ul>
|
</ul>
|
||||||
</Column1>
|
</Column1>
|
||||||
<Column2>
|
<Column2>
|
||||||
<ul>
|
<ul>
|
||||||
<li> Notebook 11.1 - Shattered gradients: <a
|
<li> Notebook 11.1 - Shattered gradients: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 11.2 - Residual networks: <a
|
<li> Notebook 11.2 - Residual networks: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 11.3 - Batch normalization: <a
|
<li> Notebook 11.3 - Batch normalization: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 12.1 - Self-attention: <a
|
<li> Notebook 12.1 - Self-attention: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 12.2 - Multi-head self-attention: <a
|
<li> Notebook 12.2 - Multi-head self-attention: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 12.3 - Tokenization: <a
|
<li> Notebook 12.3 - Tokenization: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 12.4 - Decoding strategies: <a
|
<li> Notebook 12.4 - Decoding strategies: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 13.1 - Encoding graphs: <a
|
<li> Notebook 13.1 - Encoding graphs: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 13.2 - Graph classification : <a
|
<li> Notebook 13.2 - Graph classification : <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 13.3 - Neighborhood sampling: <a
|
<li> Notebook 13.3 - Neighborhood sampling: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 13.4 - Graph attention: <a
|
<li> Notebook 13.4 - Graph attention: <NBLink
|
||||||
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </a>
|
href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </NBLink>
|
||||||
</li>
|
</li>
|
||||||
<li> Notebook 15.1 - GAN toy example: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </a></li>
|
<li> Notebook 15.1 - GAN toy example: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 15.2 - Wasserstein distance: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </a></li>
|
<li> Notebook 15.2 - Wasserstein distance: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 16.1 - 1D normalizing flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </a></li>
|
<li> Notebook 16.1 - 1D normalizing flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 16.2 - Autoregressive flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </a></li>
|
<li> Notebook 16.2 - Autoregressive flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 16.3 - Contraction mappings: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </a></li>
|
<li> Notebook 16.3 - Contraction mappings: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 17.1 - Latent variable models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </a></li>
|
<li> Notebook 17.1 - Latent variable models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 17.2 - Reparameterization trick: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </a></li>
|
<li> Notebook 17.2 - Reparameterization trick: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 17.3 - Importance sampling: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </a></li>
|
<li> Notebook 17.3 - Importance sampling: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 18.1 - Diffusion encoder: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </a></li>
|
<li> Notebook 18.1 - Diffusion encoder: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 18.2 - 1D diffusion model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </a></li>
|
<li> Notebook 18.2 - 1D diffusion model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 18.3 - Reparameterized model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </a></li>
|
<li> Notebook 18.3 - Reparameterized model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 18.4 - Families of diffusion models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </a></li>
|
<li> Notebook 18.4 - Families of diffusion models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 19.1 - Markov decision processes: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </a></li>
|
<li> Notebook 19.1 - Markov decision processes: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 19.2 - Dynamic programming: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </a></li>
|
<li> Notebook 19.2 - Dynamic programming: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 19.3 - Monte-Carlo methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </a></li>
|
<li> Notebook 19.3 - Monte-Carlo methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 19.4 - Temporal difference methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </a></li>
|
<li> Notebook 19.4 - Temporal difference methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 19.5 - Control variates: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </a></li>
|
<li> Notebook 19.5 - Control variates: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 20.1 - Random data: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </a></li>
|
<li> Notebook 20.1 - Random data: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 20.2 - Full-batch gradient descent: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </a></li>
|
<li> Notebook 20.2 - Full-batch gradient descent: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 20.3 - Lottery tickets: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </a></li>
|
<li> Notebook 20.3 - Lottery tickets: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 20.4 - Adversarial attacks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </a></li>
|
<li> Notebook 20.4 - Adversarial attacks: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 21.1 - Bias mitigation: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </a></li>
|
<li> Notebook 21.1 - Bias mitigation: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </NBLink></li>
|
||||||
<li> Notebook 21.2 - Explainability: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </a></li>
|
<li> Notebook 21.2 - Explainability: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </NBLink></li>
|
||||||
</ul>
|
</ul>
|
||||||
</Column2>
|
</Column2>
|
||||||
</NotebookRow>
|
</NotebookRow>
|
||||||
|
|||||||
23
style.css
23
style.css
@@ -1,23 +0,0 @@
|
|||||||
body {
|
|
||||||
font-size: 17px;
|
|
||||||
margin: 2% 10%;
|
|
||||||
}
|
|
||||||
|
|
||||||
#head {
|
|
||||||
display: flex;
|
|
||||||
flex-direction: row;
|
|
||||||
flex-wrap: wrap-reverse;
|
|
||||||
justify-content: space-between;
|
|
||||||
width: 100%;
|
|
||||||
}
|
|
||||||
|
|
||||||
#cover {
|
|
||||||
justify-content: center;
|
|
||||||
display: flex;
|
|
||||||
width: 30%;
|
|
||||||
}
|
|
||||||
|
|
||||||
#cover img {
|
|
||||||
width: 100%;
|
|
||||||
height: min-content;
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user