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CNN Intro presentaiton: Add first draft
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Martin Thoma committed Jan 26, 2019
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119 changes: 119 additions & 0 deletions presentations/CNN-Intro/CNN-Intro.tex
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\documentclass{beamer}
\usetheme{metropolis}
\usepackage{hyperref}
\usepackage[utf8]{inputenc} % this is needed for german umlauts
\usepackage[english]{babel} % this is needed for german umlauts
\usepackage[T1]{fontenc} % this is needed for correct output of umlauts in pdf
\usepackage{tikz}
\usetikzlibrary{arrows.meta}
\usetikzlibrary{decorations.pathreplacing}
\usetikzlibrary{positioning}
\usetikzlibrary{decorations.text}
\usetikzlibrary{decorations.pathmorphing}
\usetikzlibrary{shapes.multipart, calc}

\begin{document}

\title{Convolutional Neural Networks (CNNs)}
\subtitle{Theory and Applications}
\author{Martin Thoma}
\date{22. February 2019}
\subject{Machine Learning, AI, Neural Networks, Convolutional Neural Networks}

\frame{\titlepage}

% \section{Neural Network Basics}
% \subsection{}
\begin{frame}{Artificial Neuron (Perceptron)}
$$f: \mathbb{R}^n \rightarrow \mathbb{R}$$
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/artificial-neuron.pdf}
\end{figure}
% $$f(x) = ax^2 + bx + c \text{ with } f(0) = 3, f(1) = 2, f(-1) = 6$$
% \begin{align*}
% \onslide<2->{f(0) &= a \cdot 0^2 + b \cdot 0 + c = 3} &\onslide<3->{\Rightarrow c &= 3\\}
% \onslide<4->{f(1) &= a \cdot 1^2 + b \cdot 1 + 3 = 2} &\onslide<5->{\Rightarrow a &= -1-b\\}
% \onslide<6->{f(-1) &= a \cdot {(-1)}^2 - b + 3 = 6\\}
% \onslide<7->{\Leftrightarrow 3&=a - b\\}
% \onslide<8->{\Leftrightarrow 3&= (-1-b) - b\\}
% \onslide<9->{\Leftrightarrow b&= -2\\}
% \onslide<10>{\Rightarrow \quad f(x) &= x^2 -2 x + 3\\}
% \end{align*}
% \only<1>{$$f: \mathbb{R}^n \rightarrow \mathbb{R}^m$$}
% \only<2>{$$f: \mathbb{R}^2 \rightarrow \mathbb{R}$$
% # 2x - 1
% # (x-1)^2 + 1
% Examples:
% \begin{itemize}
% \item $1 \rightarrow 1$: $f(x) = x$
% \item $2 \rightarrow 3$: $f(x) = $
% % \item $3 \rightarrow 3$
% \end{itemize}
% }
\end{frame}

\begin{frame}{Multi-Layer Perceptron (MLP)}
$$f: \mathbb{R}^n \rightarrow \mathbb{R}^m$$
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/perceptron-notation.pdf}
\end{figure}
\end{frame}

\begin{frame}{}
\begin{itemize}[<+->]
\item Predict housing prices: (bed rooms, size, age) $\rightarrow$ Price
\item Product categorization: (weight, volume, price) $\rightarrow$ \{shoe, handbag, shirt\}
\item Image classification: List of pixel colors $\rightarrow$ \{cat, dog\}
\end{itemize}
\end{frame}

\begin{frame}{}
\begin{center}
\Huge Data
\end{center}
\end{frame}

\begin{frame}{Necessary Data}
\begin{itemize}
\item $f(x) = w_0$
\item $f(x) = w_1 \cdot x + w_0$
\item $f(x) = w_2^2 \cdot x^2 + w_1^2 \cdot x + w_0$
\item sin, cos, tan, \dots
\end{itemize}
\end{frame}

\begin{frame}{Convolution}
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\paperwidth]{graphics/convolution-linear.pdf}
\end{figure}
\end{frame}

\begin{frame}{Convolutional Layer}
\begin{figure}[ht]
\centering
\input{graphics/convolution-layer}
\end{figure}
\end{frame}

\begin{frame}{Max Pooling}
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\paperwidth]{graphics/max-pooling.pdf}
\end{figure}
\end{frame}

\section{Applications}
\begin{frame}{Symbol recognizer}
\begin{center}
\href{http://write-math.com}{write-math.com}
\end{center}
\end{frame}

\begin{frame}{Symbol recognizer}
GANs
\end{frame}

\end{document}
8 changes: 8 additions & 0 deletions presentations/CNN-Intro/Makefile
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SOURCE = CNN-Intro

make:
pdflatex $(SOURCE).tex -output-format=pdf
make clean

clean:
rm -rf $(TARGET) *.class *.html *.log *.aux *.out *.glo *.glg *.gls *.ist *.xdy *.1 *.toc *.snm *.nav *.vrb *.fls *.fdb_latexmk *.pyg
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138 changes: 138 additions & 0 deletions presentations/CNN-Intro/beamercolorthememetropolis.sty
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%%
%% and the original template was based on the HSRM theme by Benjamin Weiss.
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