Add handout for 'art in ml' presentation

documents/handout-art-in-ml/Makefile

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+SOURCE = handout-art-in-ml
+make:
+	pdflatex $(SOURCE).tex -output-format=pdf
+	make clean
+
+clean:
+	rm -rf  $(TARGET) *.class *.html *.log *.aux *.out

documents/handout-art-in-ml/activation-functions.tikz

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+\begin{tikzpicture}[scale=1.0]
+    \begin{axis}[
+        legend pos=north west,
+        axis x line=middle,
+        axis y line=middle,
+        grid = major,
+        width=16cm,
+        height=4cm,
+        grid style={dashed, gray!30},
+        xmin=-5,     % start the diagram at this x-coordinate
+        xmax= 5,    % end   the diagram at this x-coordinate
+        ymin=-1,     % start the diagram at this y-coordinate
+        ymax= 1,   % end   the diagram at this y-coordinate
+        %axis background/.style={fill=white},
+        xlabel=$x$,
+        ylabel=$y$,
+        tick align=outside,
+        enlargelimits=true]
+      \addplot[green!50!black, ultra thick] coordinates {(-5,-1) (0,-1) (0, 1) (5, 1)};
+      % \addplot[domain=-5:5, green!50!black, ultra thick,samples=500] {x < 0 ? -1 : 1};
+      \addplot[domain=-5:5, red, ultra thick,samples=500, dash pattern=on 8pt off 2pt] {1/(1+exp(-x))};
+      \addplot[domain=-5:5, blue, ultra thick,samples=500, dotted] {tanh(x)};
+      \addlegendentry{sign function}
+      \addlegendentry{$\sigmoid$}
+      \addlegendentry{$\tanh$}
+    \end{axis}
+\end{tikzpicture}

documents/handout-art-in-ml/feed-forward-nn.tikz

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+\tikzstyle{input}=[draw,fill=red!50,circle,minimum size=10pt,inner sep=0pt]
+\tikzstyle{hidden}=[draw,fill=green!50,circle,minimum size=10pt,inner sep=0pt]
+\tikzstyle{output}=[draw,fill=blue!50,circle,minimum size=10pt,inner sep=0pt]
+\tikzstyle{bias}=[draw,dashed,fill=gray!50,circle,minimum size=10pt,inner sep=0pt]
+
+\begin{tikzpicture}
+  \node (b1)[bias] at (-1,0) {};
+  \node (b2)[bias] at (-0.5,1) {};
+  \node (i1)[input] at (0,0) {};
+  \node (i2)[input] at (1,0) {};
+  \node (i3)[input] at (2,0) {};
+  \node (i4)[input] at (3,0) {};
+  \node (i5)[input] at (4,0) {};
+  \node (h1)[hidden] at (0.5,1) {};
+  \node (h2)[hidden] at (1.5,1) {};
+  \node (h3)[hidden] at (2.5,1) {};
+  \node (o1)[output] at (1.5,2) {};
+
+    \draw[->] (b2) -- (o1);
+    \draw[->] (h1) -- (o1);
+    \draw[->] (h2) -- (o1);
+    \draw[->] (h3) -- (o1);
+
+    \foreach \j in {1, ..., 3}{
+        \foreach \i in {1, ..., 5}{
+            \draw[<-] (h\j) -- (i\i);
+        }
+        \draw[<-] (h\j) -- (b1);
+    }
+\end{tikzpicture}

documents/handout-art-in-ml/handout-art-in-ml.tex

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+\documentclass[a4paper,9pt]{scrartcl}
+\usepackage{amssymb, amsmath} % needed for math
+\usepackage[utf8]{inputenc} % this is needed for umlauts
+\usepackage[ngerman]{babel} % this is needed for umlauts
+\usepackage[T1]{fontenc}    % this is needed for correct output of umlauts in pdf
+\usepackage[margin=2.0cm]{geometry} %layout
+\usepackage{hyperref}   % links im text
+\usepackage{enumerate}  % for advanced numbering of lists
+\usepackage{color}
+\usepackage{framed}
+\usepackage{float}
+\usepackage{caption}
+\usepackage{csquotes}
+\usepackage[hang]{subfigure}
+\usepackage[pdftex,final]{graphicx}
+\usepackage{pgfplots}
+\usepackage{tikz}
+\usepackage{tikzscale}
+\usetikzlibrary{shapes, calc, shapes, arrows}
+\DeclareMathOperator{\sigmoid}{sigmoid}
+
+\newcommand\titleText{Kreativität im maschinellen Lernen}
+\title{\vspace{-5ex}\titleText\vspace{-7ex}}
+\author{}
+\date{}
+\hypersetup{
+  pdfauthor   = {Martin Thoma},
+  pdfkeywords = {Machine Learning, Art, Creativity},
+  pdftitle    = {\titleText}
+}
+
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\fancyhead{}
+\fancyfoot{}
+\fancyhead[L]{Referat vom 15.01.2016}
+\fancyhead[R]{Martin Thoma}
+\renewcommand{\headrulewidth}{0.4pt}
+
+\makeatletter
+\let\ps@plain\ps@fancy
+\makeatother
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Custom definition style, by                                       %
+% http://mathoverflow.net/questions/46583/what-is-a-satisfactory-way-to-format-definitions-in-latex/58164#58164
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\makeatletter
+\newdimen\errorsize \errorsize=0.2pt
+% Frame with a label at top
+\newcommand\LabFrame[2]{%
+    \fboxrule=\FrameRule
+    \fboxsep=-\errorsize
+    \textcolor{FrameColor}{%
+    \fbox{%
+      \vbox{\nobreak
+      \advance\FrameSep\errorsize
+      \begingroup
+        \advance\baselineskip\FrameSep
+        \hrule height \baselineskip
+        \nobreak
+        \vskip-\baselineskip
+      \endgroup
+      \vskip 0.5\FrameSep
+      \hbox{\hskip\FrameSep \strut
+        \textcolor{TitleColor}{\textbf{#1}}}%
+      \nobreak \nointerlineskip
+      \vskip 1.3\FrameSep
+      \hbox{\hskip\FrameSep
+        {\normalcolor#2}%
+        \hskip\FrameSep}%
+      \vskip\FrameSep
+    }}%
+}}
+\definecolor{FrameColor}{rgb}{0.25,0.25,1.0}
+\definecolor{TitleColor}{rgb}{1.0,1.0,1.0}
+
+\newenvironment{contlabelframe}[2][\Frame@Lab\ (cont.)]{%
+  % Optional continuation label defaults to the first label plus
+  \def\Frame@Lab{#2}%
+  \def\FrameCommand{\LabFrame{#2}}%
+  \def\FirstFrameCommand{\LabFrame{#2}}%
+  \def\MidFrameCommand{\LabFrame{#1}}%
+  \def\LastFrameCommand{\LabFrame{#1}}%
+  \MakeFramed{\advance\hsize-\width \FrameRestore}
+}{\endMakeFramed}
+\newcounter{definition}
+\newenvironment{definition}[1]{%
+  \par
+  \refstepcounter{definition}%
+  \begin{contlabelframe}{Definition \thedefinition:\quad #1}
+ \noindent\ignorespaces}
+{\end{contlabelframe}}
+\makeatother
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Begin document                                                    %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document}
+\maketitle
+\begin{definition}{Machine Learning (ML) nach Tom Mitchell}
+A computer program is said to learn from \textbf{experience}~$\mathbf{E}$ with
+respect to some class of \textbf{tasks}~$\mathbf{T}$ and \textbf{performance
+measure}~$\mathbf{P}$, if its performance at tasks in~$T$, as measured by~$P$,
+improves with experience~$E$.
+\end{definition}
+
+\begin{figure}[H]
+\centering
+\subfigure[Aufbau eines künstlichen Neurons. Die Eingabesignale werden mit $x_i \in \mathbb{R}$ bezeichnet; $w_i \in \mathbb{R}$ heißen \textit{Gewichte} und müssen gelernt werden. Jedes Eingabesignal wird mit seinem Gewicht multipliziert. Die Produkte werden aufsummiert. Dann wird die sog. \textit{Aktivierungsfuntkion} $i$ angewendet.]{
+  \label{fig:artificial-neuron}
+  \includegraphics[width=0.45\linewidth]{neuron.tikz}
+}%
+\subfigure[Eine einfaches Feed-Forward Neuronales Netz. Die 5~Eingabeneuronen sind rot, die 2~Bias-Neuronen sind Grau, die 3~Hidden-Neuronen sind Grün und das einzelne Ausgabeneuron ist Blau. Dieses 3-schichtige Modell hat $6 \cdot 4 + 4 \cdot 1 = 28$ Kanten. Für jede Kante muss ein Gewicht $w_{ij} \in \mathbb{R}$ gelernt werden.]{
+  \label{fig:feed-forward-nn}
+  \includegraphics[width=0.45\linewidth]{feed-forward-nn.tikz}
+}
+
+\subfigure[Beispiele für Aktivierungsfuntkionen $\varphi: \mathbb{R} \rightarrow \mathbb{R}$]{
+  \label{fig:artificial-neuron}
+  \includegraphics[width=0.9\linewidth]{activation-functions.tikz}
+}%
+
+\caption{Neuronale Netze basieren auf einfachen Einheiten, welche zu komplexen Netzwerken verschaltet werden können. Diese können mittels \textit{Gradientenabstieg} automatisch trainiert werden.}
+\label{fig:neural-style}
+\end{figure}
+
+\begin{definition}{Convolutional Neural Network (CNN)}
+Ein CNN ist ein neuronales Netz, welches keine vollverbundenen Schichten hat
+sondern die Gewichte von Bildfiltern lernt.
+\end{definition}
+
+\begin{definition}{Rekurrentes Neuronale Netz (RNN)}
+Ein RNN ist ein neuronales Netz, welches Kanten hat, die zeitlich versetzt
+wieder als Eingabe genutzt werden.
+\end{definition}
+
+CNNs können sehr effektiv für Bilder eingesetzt werden, RNNs können zur
+Behandlung von Sequenzen verwendet werden. Insbesondere können beliebig lange
+Eingabesequenzen genutzt werden und unabhängig von der Eingabe beliebig lange
+Ausgaben erzeugt werden.
+
+\begin{definition}{Google DeepDream}
+Google DeepDream ist eine Abwandlung einer Technik zur Analyse der gelernten
+Gewichte.
+\end{definition}
+
+\section*{Quellen}
+Alle Quellen und eine detailierte Beschreibung der Verfahren sind unter\\
+\url{https://github.com/MartinThoma/seminar-art-in-machine-learning} sowie im arXiv unter\\
+\enquote{Creativity in Machine Learning} --- \url{http://arxiv.org/abs/1601.03642} ---
+zu finden.
+
+\end{document}

documents/handout-art-in-ml/neuron.tikz

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+\tikzstyle{inputNode}=[draw,circle,minimum size=10pt,inner sep=0pt]
+\tikzstyle{stateTransition}=[->, thick]
+
+\begin{tikzpicture}
+    \node[draw,circle,minimum size=25pt,inner sep=0pt] (x) at (0,0) {$\Sigma$ $\varphi$};
+
+    \node[inputNode] (x0) at (-2, 0.75) {$\tiny x_0$};
+    \node[inputNode] (x1) at (-2, 0.375) {$\tiny x_1$};
+    \node[inputNode] (x2) at (-2, 0) {$\tiny x_2$};
+    \node[inputNode] (x3) at (-2, -0.375) {$\tiny x_3$};
+    \node[inputNode] (xn) at (-2, -0.9) {$\tiny x_n$};
+
+    \draw[stateTransition] (x0) to[out=0,in=120] node [midway, sloped, above=-2] {$w_0$} (x);
+    \draw[stateTransition] (x1) to[out=0,in=150] node [midway, sloped, above=-2] {$w_1$} (x);
+    \draw[stateTransition] (x2) to[out=0,in=180] node [midway, sloped, above=-2] {$w_2$} (x);
+    \draw[stateTransition] (x3) to[out=0,in=210] node [midway, sloped, above=-2] {$w_3$} (x);
+    \draw[stateTransition] (xn) to[out=0,in=240] node [midway, sloped, above=-2] {$w_n$} (x);
+    \draw[stateTransition] (x) -- (1,0) node [midway,above=-0.1cm] {};
+    \draw[dashed] (0,-0.43) -- (0,0.43);
+    \node (dots) at (-2, -0.57) {$\vdots$};
+\end{tikzpicture}