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Updates slides.
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‎slides/slides.pdf‎

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‎slides/slides.tex‎

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\usepackage[T1]{fontenc}
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\usepackage{tikz}
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\usetikzlibrary{positioning}
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\usetikzlibrary{matrix, positioning}
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\usetikzlibrary{decorations.pathreplacing}
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\usepackage{etoolbox}
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\usepackage{listofitems} % for \readlist to create arrays
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\frame{\tableofcontents}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ----------- Intro al DL -------------------
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ----------- Intro al DL --------------------
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Intro al aprendizaje profundo}
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% ----------- Motivación 01 ------------------
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\texttt{history = model.fit(..., callbacks=[callback])}
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\end{frame}
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% Mover/eliminar conforme se agregan los contenidos
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\subsection{Regularización}
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\section{Visión computacional profunda}
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\section{Modelado profundo de secuencias}
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\section{Modelado generativo profundo}
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\section{Panorama actual y futuro}
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% ----------- Lecturas recomendadas 03 ------
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\begin{frame}{Lecturas recomendadas}{Intro al aprendizaje profundo}
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\begin{itemize}
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\item \colorbox{blue!10}{\href{https://neptune.ai/blog/vanishing-and-exploding-gradients-debugging-monitoring-fixing}{Vanishing and Exploding Gradients in Neural Network Models:}} Debugging, Monitoring, and Fixing
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\end{itemize}
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\end{frame}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ----------- Computer Vision ----------------
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Visión computacional profunda}
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% ----------- Intro a imágenes 01 ------------
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\subsection{Introducción a imágenes}
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\begin{frame}{¿Qué es una imagen?}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.75\textwidth]{img-deer}
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\end{figure}
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\end{frame}
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% ----------- Intro a imágenes 02 ------------
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\begin{frame}{¿Qué es una imagen?}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.75\textwidth]{img-lincoln}
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\let\thefootnote\relax\footnote{{\tiny “Tutorial 1: Image Filtering.” AI Stanford. \url{https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html}}}
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\end{figure}
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\end{frame}
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% ----------- Intro a imágenes 03 ------------
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\begin{frame}{¿Qué es una imagen?}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.75\textwidth]{img-lena}
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\let\thefootnote\relax\footnote{{\tiny “Tutorial 1: Image Filtering.” AI Stanford. \url{https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html}}}
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\end{figure}
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\end{frame}
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% ----------- Intro a imágenes 04 ------------
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\begin{frame}{¿Qué es una imagen?}{Visión computacional profunda}
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\begin{itemize}
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\item Una imagen es un arreglo de pixeles, la cual peude tener 1 o más canales de color. Usualmente:
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\begin{itemize}
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\item 1 canal de color $\rightarrow$ Escala de grises
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\item 3 canales de color $\rightarrow$ Escala RGB
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\item 4 canales de color $\rightarrow$ Escala RGBA
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\end{itemize}
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\item Un pixel puede ser visto como un objeto 5-dimensional $(x, y, r, g, b)$.
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\end{itemize}
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\end{frame}
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% ----------- Biología humana 01 -------------
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\begin{frame}{La biología humana}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.8\textwidth]{img-encroma-01}
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\let\thefootnote\relax\footnote{{\tiny “How EnChroma Color Blind Glasses Work.” EnChroma. \url{http://enchroma.com/technology/}}}
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\end{figure}
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\end{frame}
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% ----------- Biología humana 02 -------------
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\begin{frame}{La biología humana}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.8\textwidth]{img-encroma-02}
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\let\thefootnote\relax\footnote{{\tiny “How EnChroma Color Blind Glasses Work.” EnChroma. \url{http://enchroma.com/technology/}}}
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\end{figure}
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\end{frame}
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% ----------- Espacios de color --------------
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\subsection{Espacios de color}
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\begin{frame}{Espacios de color}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{img-color-space}
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\end{figure}
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\end{frame}
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% ----------- Convoluciones 01 ---------------
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\subsection{Convoluciones \& Pooling}
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\begin{frame}{¿Qué es una convolución?}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{1d-conv}
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\end{figure}
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\end{frame}
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% ----------- Ejercicio 05 ------------------
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\begin{frame}{Ejercicio}{Intro al aprendizaje profundo}
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\begin{center}
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{\Large \textbf{Ejercicio: Introducción a imágenes}}
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\end{center}
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\begin{figure}
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\centering
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\includegraphics[width=0.6\textwidth]{img-lena}
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\end{figure}
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\end{frame}
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% ----------- Convoluciones 02 ---------------
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\begin{frame}{¿Qué es una convolución?}{Visión computacional profunda}
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\begin{center}
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\begin{tikzpicture}[
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2d-arr/.style={matrix of nodes, row sep=-\pgflinewidth, column sep=-\pgflinewidth, nodes={draw}}, ampersand replacement=\&
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]
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\matrix (mtr) [2d-arr] {
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0 \& 1 \& 1 \& |[fill=orange!30]| 1 \& |[fill=orange!30]| 0 \& |[fill=orange!30]| 0 \& 0\\
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0 \& 0 \& 1 \& |[fill=orange!30]| 1 \& |[fill=orange!30]| 1 \& |[fill=orange!30]| 0 \& 0\\
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0 \& 0 \& 0 \& |[fill=orange!30]| 1 \& |[fill=orange!30]| 1 \& |[fill=orange!30]| 1 \& 0\\
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0 \& 0 \& 0 \& 1 \& 1 \& 0 \& 0\\
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0 \& 0 \& 1 \& 1 \& 0 \& 0 \& 0\\
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0 \& 1 \& 1 \& 0 \& 0 \& 0 \& 0\\
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1 \& 1 \& 0 \& 0 \& 0 \& 0 \& 0\\
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};
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\node[below=of mtr-5-4] {$\mathbf I$};
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\node[right=0.2em of mtr] (str) {$*$};
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\matrix (K) [2d-arr, right=0.2em of str, nodes={draw, fill=teal!30}] {
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1 \& 0 \& 1 \\
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0 \& 1 \& 0 \\
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1 \& 0 \& 1 \\
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};
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\node[below=of K-3-2] {$\mathbf K$};
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\node[right=0.2em of K] (eq) {$=$};
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\matrix (ret) [2d-arr, right=0.2em of eq] {
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1 \& 4 \& 3 \& |[fill=blue!80!black!30]| 4 \& 1\\
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1 \& 2 \& 4 \& 3 \& 3\\
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1 \& 2 \& 3 \& 4 \& 1\\
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1 \& 3 \& 3 \& 1 \& 1\\
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3 \& 3 \& 1 \& 1 \& 0\\
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};
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\node[below=of ret-4-3] {$\mathbf{I * K}$};
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\draw[dashed, teal] (mtr-1-6.north east) -- (K-1-1.north west);
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\draw[dashed, teal] (mtr-3-6.south east) -- (K-3-1.south west);
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\draw[dashed, blue!80!black] (K-1-3.north east) -- (ret-1-4.north west);
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\draw[dashed, blue!80!black] (K-3-3.south east) -- (ret-1-4.south west);
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\foreach \i in {1,2,3} {
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\foreach \j in {4,5,6} {
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\node[font=\tiny, scale=0.6, shift={(-1.2ex,-2ex)}] at (mtr-\i-\j) {$\times \pgfmathparse{int(mod(\i+\j,2))}\pgfmathresult$};
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}
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}
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\end{tikzpicture}
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\end{center}
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\end{frame}
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% ----------- Convoluciones 03 ---------------
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\begin{frame}{¿Qué es una convolución?}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{filter}
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\end{figure}
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\let\thefootnote\relax\footnote{{\tiny “Image Kernels.” Victor Powell. \url{https://setosa.io/ev/image-kernels/}}}
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\end{frame}
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% ----------- Pooling 01 ---------------------
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\begin{frame}{Pooling}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{pooling}
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\end{figure}
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\end{frame}
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% ----------- Ejercicio 06 ------------------
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\begin{frame}{Ejercicio}{Intro al aprendizaje profundo}
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\begin{center}
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{\Large \textbf{Ejercicio: Convoluciones \& Pooling}}
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\end{center}
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\begin{figure}
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\centering
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\includegraphics[width=0.6\textwidth]{filter}
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\end{figure}
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\end{frame}
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% ----------- CNNs 01 ------------------------
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\subsection{Redes neuronales convolucionales}
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\begin{frame}{Redes neuronales convolucionales}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{lecun}
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\caption{Yann LeCun}
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\end{figure}
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\end{frame}
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% ----------- CNNs 02 ------------------------
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\begin{frame}{Redes neuronales convolucionales}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{cnns-01}
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\end{figure}
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\end{frame}
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% ----------- CNNs 03 ------------------------
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\begin{frame}{Redes neuronales convolucionales}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{cnns-02}
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\end{figure}
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\end{frame}
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% ----------- CNNs 04 ------------------------
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\begin{frame}{Redes neuronales convolucionales}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{cnns-03}
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\end{figure}
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\end{frame}
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% ----------- CNNs 05 ------------------------
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\subsection{Clasificadores de imágenes (LeNet, VGG16, etc.)}
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\begin{frame}{LeNet-5}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{classifiers-01}
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\end{figure}
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\end{frame}
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% ----------- CNNs 06 ------------------------
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\begin{frame}{VGG16}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{classifiers-02}
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\end{figure}
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\end{frame}
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% ----------- CNNs 07 ------------------------
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\begin{frame}{Resnet50}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{classifiers-03}
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\end{figure}
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\end{frame}
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% ----------- CNNs 08 ------------------------
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\begin{frame}{GoogLeNet}{Visión computacional profunda}
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\begin{figure}
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\centering
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\includegraphics[width=0.9\textwidth]{classifiers-04}
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\end{figure}
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\end{frame}
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% ----------- Ejercicio 07 ------------------
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\begin{frame}{Ejercicio}{Intro al aprendizaje profundo}
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\begin{center}
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{\Large \textbf{Ejercicio: Redes neuronales convolucionales}}
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\end{center}
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\begin{figure}
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\centering
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\includegraphics[width=0.6\textwidth]{classifiers-01}
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\end{figure}
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\end{frame}
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% Mover/eliminar conforme se agregan los contenidos
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\section{Modelado profundo de secuencias}
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\section{Modelado generativo profundo}
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\section{Panorama actual y futuro}
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% ----------- Lecturas recomendadas 04 ------
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\begin{frame}{Lecturas recomendadas}{Visión computacional}
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\begin{itemize}
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\item Tutorial 1: \colorbox{blue!10}{\href{https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html}{Image Filtering}}
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\item \colorbox{blue!10}{\href{https://setosa.io/ev/image-kernels/}{Image Kernels Explained Visually}} by Victor Powell
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\item \colorbox{blue!10}{\href{https://gudgud96.github.io/2020/11/25/param-pooling/}{Parameterized Pooling Layers}} by Hao Hao Tan
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\item TensorFlow Tutorials: \colorbox{blue!10}{\href{https://www.tensorflow.org/tutorials/images}{Vision}}
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\end{itemize}
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\end{frame}
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\end{document}

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