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Updated chapter_1_introduction
QR code has been added to video blocks. Unnecessary space after the footnote mark has been removed.
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contents/core/introduction/introduction.qmd

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@@ -204,13 +204,33 @@ The deep learning revolution of 2012 didn't emerge from nowhere, as it was found
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[^fn-cnn]: **Convolutional Neural Network (CNN)**: A type of neural network specially designed for processing images, inspired by how the human visual system works. The "convolutional" part refers to how it scans images in small chunks, similar to how our eyes focus on different parts of a scene.
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:::{#vid-tl .callout-important title="Convolutional Network Demo from 1989"}
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Yet these networks largely languished through the 1990s and 2000s, not because the ideas were wrong, but because they were ahead of their time---the field lacked three important ingredients: sufficient data to train complex networks, enough computational power to process this data, and the technical innovations needed to train very deep networks effectively.
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:::{.callout-important title="Convolutional Network Demo from 1989"}
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::: {.content-visible when-format="html"}
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{{< video https://www.youtube.com/watch?v=FwFduRA_L6Q&ab_channel=YannLeCun >}}
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:::
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Yet these networks largely languished through the 1990s and 2000s, not because the ideas were wrong, but because they were ahead of their time---the field lacked three important ingredients: sufficient data to train complex networks, enough computational power to process this data, and the technical innovations needed to train very deep networks effectively.
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::: {.content-visible when-format="pdf"}
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\marginnote{\centering\\\vspace*{5mm}%
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\parbox{30mm}{\centering\footnotesize%
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\textbf{Watch on YouTube}\\
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Convolutional Net Demo\\[1mm]
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}
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\begingroup
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\hypersetup{urlcolor=black}
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\qrcode[height=15mm]{https://www.youtube.com/watch?v=FwFduRA_L6Q}
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\endgroup
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\\[1mm]
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\parbox{25mm}{\centering\footnotesize%
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Scan with your phone\\
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to watch the video
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}
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}
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\faTv{} [Watch on YouTube](https://www.youtube.com/watch?v=FwFduRA_L6Q&ab_channel=YannLeCun)
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:::
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:::
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The field had to wait for the convergence of big data, better computing hardware, and algorithmic breakthroughs before deep learning's potential could be unlocked. This long gestation period helps explain why the 2012 ImageNet moment was less a sudden revolution and more the culmination of decades of accumulated research finally finding its moment. As we'll explore in the following sections, this evolution has led to two significant developments in the field. First, it has given rise to define the field of machine learning systems engineering, a discipline that teaches how to bridge the gap between theoretical advancements and practical implementation. Second, it has necessitated a more comprehensive definition of machine learning systems, one that encompasses not just algorithms, but also data and computing infrastructure. Today's challenges of scale echo many of the same fundamental questions about computation, data, and learning methods that researchers have grappled with since the field's inception, but now within a more complex and interconnected framework.
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tex/header-includes.tex

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\usepackage{amsmath}
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\usepackage{amssymb}
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\usepackage{enumitem}
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\usepackage{fontawesome5}
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\usepackage{qrcode}
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\qrset{link, height=15mm}
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% Define the Crimson color
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\definecolor{crimson}{HTML}{A51C30}
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\hspace{0.3em} % Spacing between the line and the sidenote text
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\color{black} % Reset color for sidenote text
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{\footnotesize #1} % Sidenote text in smaller font size
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}
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}%
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}
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% Redefine the figure environment (fixes the bug where even page captions don't show, odd I know!)
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\else
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\setcounter{page}{\value{page}}%
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\fi
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\setcounter{footnote}{0}% Reset footnote counter
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\setcounter{sidenote}{0}% Reset footnote counter
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\old@chapter{#1}%
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}
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\pagenumbering{roman}%
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\@firstunnumberedfalse%
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\fi
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\setcounter{footnote}{0}% Reset footnote counter
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\setcounter{sidenote}{0}% Reset footnote counter
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\old@chapter*{#1}%
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}
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\makeatother

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