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finished chapter 2

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Clemens Dautermann 2020-01-25 16:42:10 +01:00
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doc/Grundlagen_des_maschinellen_lernens.tex
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@ -96,10 +96,15 @@ Overfitting tritt auf, wenn man ein neuronales Netz zu lange auf einem Datensatz
Um Overfitting entgegenzuwirken reicht es oftmals den Trainingsdatensatz in der Reihenfolge zu randomisieren. Dadurch kann das Netz diese gar nicht auswendig lernen. Um Overfitting entgegenzuwirken reicht es oftmals den Trainingsdatensatz in der Reihenfolge zu randomisieren. Dadurch kann das Netz diese gar nicht auswendig lernen.
\section{Verschiedene Techniken maschinellen Lernens} \section{Verschiedene Techniken maschinellen Lernens}
Es gibt viele verschiedene Ansätze und Algorythmen um maschinelles Lernen zu implementieren. Der wohl häufigste ist das Neuronale Netz, von dem diese Arbeit handelt. Aber auch sogenannte \glqq Support Vector machines'' sind eine bekannte Technik. Neuronale Netze können In vielen verschiedenen Szenarien angewandt werden um unterschiedliche Ergebnisse zu erzielen. Beim Adversarial Learning lässt man mehrere Netze gegen einander antreten, sodass sie sich gegenseitig trainieren. Beim Q-Learning beginnt man mit zufälligen Reaktionen auf eine Eingabe und \glqq belohnt'' das Netz, falls es wie gewünmscht reagiert hat. Ein Beispiel hierfür ist die hide and seek AI von OpenAI\footnote{https://openai.com/blog/emergent-tool-use/}. Im groben unterscheidet man jedoch in überwachtes (supervised), unüberwachtes (unsupervised) und bestärkendes (reinforcement) Lernen.
\subsection{Überwachtes Lernen} \subsection{Überwachtes Lernen}
Beim überwachten Lernen ist ein Trainingsdatensatz vorhanden und die Eingabe sowie die gewünschte Ausgabe ist bekannt. Dies trifft sowohl auf klassifizierungs, alsauch auf Regressionsprobleme zu. Um Überwachtes Lernen nutzen zu können muss man also vor allem über einen großen Datensatz verfügen. Wie genau überwachtes Lernen innerhalb des neuronalen Netzes von Statten geht ist im nächsten Abschnitt unter \glqq\nameref{sec:neuronale-netze}'' beschrieben.
\subsection{Unüberwachtes Lernen} \subsection{Unüberwachtes Lernen}
Unüberwachtes Lernen erkennt automatisiert Muster in Datenmengen. Dies ist vergleichbar mit einem Kind, das zum ersten mal in seinem Leben einen Hund sieht und den nächsten Hund, den es sieht als einen solchen wiedererkennt. Das Kind hat nicht, wie es beim überwachten Lernen der Fall gewesen wäre, gesagt bekommen, dass es sich um einen Hund handelt. vielmehr hat es die Merkmale eines Hundes erkannt und sich diese gemerkt.
Bei unüberwachtem Lernen erkennt werden also automatisch Muster erkannt, die dem Algorythmus zuvor nicht mitgeteilt wurden.
\subsection{Bestärkendes Lernen} \subsection{Bestärkendes Lernen}
\section{Neuronale Netze} Bestärkendes Lernen ist die dritte Klasse von Lerntypen, die oft unterschieden werden. Hier ist kein Eingabedatensatz vorhanden, sondern ein bekanntes Ziel definiert. Der Algorythmus soll einen möglichst effizienten Weg finden dieses Ziel zu erreichen. Oft ist dabei ein Handlungsträger (agent) in einer Umgebung gegeben. Dies kann beispielsweise ein Auto sein, das lernen soll selbst zu fahren. Auch die oben erwähnte Q-Learning Methode aus open-AIs hide and seek AI fällt in die Kategorie des bestärkenden Lernens. Der agent beginnt mit zufälligen Handlungen und erhält wenn er ein Zwischenziel erreicht Belohnungen, oder Strafen wenn er einen Fehler macht. Diese Belohnungen und Strafen werden in Form von Zahlen vergeben. Das selbstfahrende Auto würde beispielsweise eine Belohnung erhalten wenn es einen Streckenabschnitt fehlerfrei zurückgelegt hat und eine Strafe, wenn es gegen eine Wand fährt. Das Ziel ist es diese kummulativen Belohnungen zu minimieren und dadurch das vordefinierte Ziel zu erreichen. Hierfür häufig verwendete Algorythmen sind das Q-learning oder sogenannte \glqq Monte-Carlo Maschinen''
\section{Neuronale Netze}\label{sec:neuronale-netze}
bei Neuronalen Netzen handelt es sich um eine programminterne Struktur, die für das maschinelle Lernen genutzt wird. Wie der Name bereits vermuten lässt, ist diese Methode ein Versuch das menschliche Lernen nachzuahmen. bei Neuronalen Netzen handelt es sich um eine programminterne Struktur, die für das maschinelle Lernen genutzt wird. Wie der Name bereits vermuten lässt, ist diese Methode ein Versuch das menschliche Lernen nachzuahmen.
\subsection{Maschinelles Lernen und menschliches Lernen} \subsection{Maschinelles Lernen und menschliches Lernen}
Das menschliche Gehirn ist aus sogenannten \glqq Neuronen'' aufgebaut. Ein Neuron ist eine Nervenzelle, die elektrische oder chemische Impulse annimmt, und gegebenenfalls einen elektrischen oder chemischen Impuls weitergibt. Die Nervenzellen berühren sich nicht direkt sondern sind nur über die sogenannten Synnapsen verbunden, über die diese Signale übertragen werden, sodass sich ein hoch komplexes Netzwerk von milliarden von Neuronen ergibt.\footnote{ Das menschliche Gehirn ist aus sogenannten \glqq Neuronen'' aufgebaut. Ein Neuron ist eine Nervenzelle, die elektrische oder chemische Impulse annimmt, und gegebenenfalls einen elektrischen oder chemischen Impuls weitergibt. Die Nervenzellen berühren sich nicht direkt sondern sind nur über die sogenannten Synnapsen verbunden, über die diese Signale übertragen werden, sodass sich ein hoch komplexes Netzwerk von milliarden von Neuronen ergibt.\footnote{

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doc/Grundlagen_des_maschinellen_lernens.toc
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\defcounter {refsection}{0}\relax \defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {2.3}Best\IeC {\"a}rkendes Lernen}{7}{subsection.2.3}% \contentsline {subsection}{\numberline {2.3}Best\IeC {\"a}rkendes Lernen}{7}{subsection.2.3}%
\defcounter {refsection}{0}\relax \defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {3.2}Der Aufbau eines neuronalen Netzes}{8}{subsection.3.2}% \contentsline {subsection}{\numberline {3.2}Der Aufbau eines neuronalen Netzes}{9}{subsection.3.2}%
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