diff --git a/jupyter/The Art of Memory Loss.html b/jupyter/The Art of Memory Loss.html
index 3b6be49..bd3f4ec 100644
--- a/jupyter/The Art of Memory Loss.html	
+++ b/jupyter/The Art of Memory Loss.html	
@@ -15142,7 +15142,8 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
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 <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/1024px-ChatGPT_logo.svg.png" />
-<img src="https://static.vecteezy.com/system/resources/previews/023/636/525/original/3d-rendering-radiation-icons-sign-caution-danger-symbol-free-png.png" />
+<img src="https://www.artandstick.be/getsupercustomizedimage.php5?objid=289&colorid1=2&colorid2=4&colorid3=4&colorid4=4&colorid5=4&way=NORMAL&transparent=Y" />    
+<!-- <img src="https://static.vecteezy.com/system/resources/previews/023/636/525/original/3d-rendering- -radiation-icons-sign-caution-danger-symbol-free-png.png" /> -->
 <img src="https://thegraphicsfairy.com/wp-content/uploads/2016/07/Frog-Drawing-Green-3-GraphicsFairy.jpg" />
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@@ -15156,7 +15157,7 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h1 id="Fr%C3%B6sche,-Wetten,-Wetter-und-Wetterfr%C3%B6sche">Fr&#246;sche, Wetten, Wetter und Wetterfr&#246;sche<a class="anchor-link" href="#Fr%C3%B6sche,-Wetten,-Wetter-und-Wetterfr%C3%B6sche">&#182;</a></h1>
+<h1 id="Fr%C3%B6sche,-Wetter-und-Wetterfr%C3%B6sche">Fr&#246;sche, Wetter und Wetterfr&#246;sche<a class="anchor-link" href="#Fr%C3%B6sche,-Wetter-und-Wetterfr%C3%B6sche">&#182;</a></h1>
 </div>
 </div>
 </div>
@@ -15217,12 +15218,12 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 </div>
 </div>
 </div>
-</div><div id="cell-id=f8a3a4d9" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div><div id="cell-id=f8a3a4d9" class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -15231,9 +15232,27 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 
 <span class="n">p</span> <span class="o">=</span> <span class="mf">0.6</span> <span class="c1"># von l nach r</span>
 <span class="n">q</span> <span class="o">=</span> <span class="mf">0.2</span> <span class="c1"># von r nach l</span>
-<span class="n">N</span> <span class="o">=</span> <span class="mi">100_000</span> <span class="c1"># Simulationen</span>
+<span class="n">N</span> <span class="o">=</span> <span class="mi">1_000_000</span> <span class="c1"># Simulationen</span>
 
 <span class="c1"># Los geht&#39;s!</span>
+<span class="n">position</span> <span class="o">=</span>  <span class="mi">0</span> <span class="c1"># 0 : links, 1: rechts</span>
+<span class="n">counter</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span> <span class="p">]</span> <span class="c1"># anzahl besuche links/rechts</span>
+<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">position</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">uniform</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">p</span><span class="p">:</span> <span class="c1"># [0.0, 0.1, ..., 1.0] &lt; 0.6 ??</span>
+            <span class="n">counter</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">position</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">position</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">counter</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">uniform</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">q</span><span class="p">:</span> <span class="c1"># [0.0, 0.1, ..., 1.0] &lt; 0.2 ??</span>
+            <span class="n">counter</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">position</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">position</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">counter</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Der Frosch war </span><span class="si">{</span><span class="n">counter</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> mal links und </span><span class="si">{</span><span class="n">counter</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> mal rechts&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;In dezimal: </span><span class="si">{</span><span class="n">counter</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">N</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> und </span><span class="si">{</span><span class="n">counter</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">N</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -15241,6 +15260,28 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 </div>
 </div>
 
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Der Frosch war 250283 mal links und 749718 mal rechts
+In dezimal: 0.2503 und 0.7497
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
 </div><div id="cell-id=41017b8e" class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
@@ -15288,8 +15329,8 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>100000/100000
-  0.2473 of the time at 0 (24728 total)
-  0.7527 of the time at 1 (75272 total)
+  0.2509 of the time at 0 (25094 total)
+  0.7491 of the time at 1 (74906 total)
 </pre>
 </div>
 </div>
@@ -15697,7 +15738,7 @@ $$
 M = \frac{1}{10} \begin{pmatrix}6 &amp; 3 &amp; 1 \\ 4 &amp; 3 &amp; 3 \\ 1 &amp; 5 &amp; 4 \end{pmatrix}
 = \begin{pmatrix}0.6 &amp; 0.3 &amp; 0.1 \\ 0.4 &amp; 0.3 &amp; 0.3 \\ 0.1 &amp; 0.5 &amp; 0.4 \end{pmatrix}
 \end{align*}
-$$<p>Wenn es heute regnet $X_0 = \begin{pmatrix}1 &amp; 0 &amp; 0 \end{pmatrix}$, ist die Wettervorschau für morgen $X_0 \cdot M$, bzw für in einer Woche $X_0 \cdot M \cdot \dots M = X_0 \cdot M^{14}$.</p>
+$$<p>Wenn heute die Sonne scheint $X_0 = \begin{pmatrix}1 &amp; 0 &amp; 0 \end{pmatrix}$, ist die Wettervorschau für morgen $X_0 \cdot M$, bzw für in zwei Wochen $X_{14} = X_0 \cdot  M \cdot \dots M = X_0 \cdot M^{14}$.</p>
 
 </div>
 </div>
@@ -15807,14 +15848,18 @@ $$
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#from sympy import symbols as S, Matrix as Mat</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span>
 
-<span class="n">M</span> <span class="o">=</span> <span class="n">array</span><span class="p">([[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]])</span>
-<span class="n">x0</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+<span class="n">M</span> <span class="o">=</span> <span class="n">array</span><span class="p">([</span>
+    <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> 
+    <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> 
+    <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]</span>
+<span class="p">)</span>
+
+<span class="n">x0</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># regen</span>
 <span class="n">x0</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span> <span class="o">@</span> <span class="n">M</span>
 </pre></div>
 
@@ -15831,13 +15876,13 @@ $$
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[29]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>array([0.40909225, 0.3484844 , 0.24242335])</pre>
+<pre>array([0.40909064, 0.34848494, 0.24242442])</pre>
 </div>
 
 </div>
@@ -15846,16 +15891,16 @@ $$
 
 </div>
 
-</div><div id="cell-id=ae437f47" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div><div id="cell-id=ae437f47" class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">linalg</span>
-<span class="n">x0</span> <span class="o">@</span> <span class="n">linalg</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="mi">14</span><span class="p">)</span>
+<span class="n">x0</span> <span class="o">@</span> <span class="n">linalg</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="c1"># schnelles Exponenzieren</span>
 </pre></div>
 
      </div>
@@ -15863,6 +15908,29 @@ $$
 </div>
 </div>
 
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[34]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([0.40909091, 0.34848485, 0.24242424])</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
 </div>
 <div id="cell-id=6efbd936" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
@@ -15892,7 +15960,7 @@ $$<p>aus. Hierbei ist $\lambda != 0$ eine Zahl, die man auch <em>Eigenwert</em>
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># linalg.eig berechnet Rechts-Eigenvektoren von Matrizen, wir suchen allerdings Links-Eigenvektoren</span>
@@ -15949,7 +16017,7 @@ $$<p>aus. Hierbei ist $\lambda != 0$ eine Zahl, die man auch <em>Eigenwert</em>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h1 id="Fr%C3%B6sche-brauchen-Zufall">Fr&#246;sche brauchen Zufall<a class="anchor-link" href="#Fr%C3%B6sche-brauchen-Zufall">&#182;</a></h1><div class="alert alert-info">
-<p>Viele Leute werfen Münzen in den Teich der Frösche.
+<p>Viele Leute werfen <strong>faire</strong> Münzen in den Teich der Frösche.
 Wir sollen einen Frosch aussuchen, der der neue Schatzmeister werden soll. 
 Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 </div>
@@ -15990,7 +16058,7 @@ Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span>
@@ -16012,7 +16080,7 @@ Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 <span class="p">[</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span>
 <span class="p">])</span>
 <span class="n">v</span> <span class="o">=</span> <span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">])</span>
-<span class="n">v</span> <span class="o">@</span> <span class="n">M</span>
+<span class="n">v</span> <span class="o">@</span> <span class="n">linalg</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="mi">7</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -16028,14 +16096,16 @@ Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[32]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[45]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>array([0. , 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
-       0. ])</pre>
+<pre>array([4.76837158e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.42857075e-01,
+       1.42857075e-01, 1.42857075e-01, 1.42857075e-01, 1.42857075e-01,
+       1.42857075e-01, 1.42857075e-01])</pre>
 </div>
 
 </div>
@@ -16053,8 +16123,9 @@ Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h1 id="Markovketten">Markovketten<a class="anchor-link" href="#Markovketten">&#182;</a></h1><p>Die Prozesse, die wir bisher gesehen haben lassen sich als <em>Markovketten</em> verstehen. Die Ketten, die wir betrachten teilen die folgenden Eigenschaften:</p>
 <ul>
-<li>Zeit vergeht für unsere Prozesse $X_1, X_2, \dots$ <strong>diskret</strong></li>
+<li>Zeit vergeht für unsere Prozesse $X_0, X_1, X_2, \dots$ <strong>diskret</strong></li>
 <li>Wir können die Übergangswahrscheinlichkeiten in einer $n \times n$ Matrix $M$ auflisten</li>
+<li>Markovketten sind gedächtnislos</li>
 </ul>
 <p>Außerdem ist $M$ eine <em>stochastische Matrix</em> und erfüllt</p>
 <ul>
@@ -16062,6 +16133,81 @@ Wie können wir aus den 7 Fröschen <strong>fair</strong> einen auswählen?</p>
 <li>jede Zeile summiert sich zu $1$</li>
 </ul>
 
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=3ca2367b" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="A:-A-simple-Language-Model">A: A simple Language Model<a class="anchor-link" href="#A:-A-simple-Language-Model">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=9598a4eb" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<div class="alert alert-info">
+<p>Mit dem <code>hp.txt</code>, generiere Harry Potter fanfiction! Ein brandneues Universum an Unsinn erwartet dich!</p>
+</div>
+
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=5dd3d18c" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="B:-Monopoly">B: Monopoly<a class="anchor-link" href="#B:-Monopoly">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=e436130d" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<div class="alert alert-info">
+<p>Wo sollte ich in Monopoly versuchen meine Häuser zu bauen?</p>
+</div>
+
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=ea10341b" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="C:-Pagerank">C: Pagerank<a class="anchor-link" href="#C:-Pagerank">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div id="cell-id=e2faef6b" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<div class="alert alert-info">
+<p>Crawle eine kleine Wiki, analysiere ihre Links und finde die <em>relevantesten</em> Seiten!</p>
+</div>
+
 </div>
 </div>
 </div>
diff --git a/jupyter/The Art of Memory Loss.ipynb b/jupyter/The Art of Memory Loss.ipynb
index 0f5e54b..5942133 100644
--- a/jupyter/The Art of Memory Loss.ipynb	
+++ b/jupyter/The Art of Memory Loss.ipynb	
@@ -50,7 +50,8 @@
     "<img src=\"https://cdn-icons-png.flaticon.com/512/3997/3997691.png\"/>\n",
     "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/Google_%22G%22_Logo.svg/2008px-Google_%22G%22_Logo.svg.png\" />\n",
     "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/1024px-ChatGPT_logo.svg.png\" />\n",
-    "<img src=\"https://static.vecteezy.com/system/resources/previews/023/636/525/original/3d-rendering-radiation-icons-sign-caution-danger-symbol-free-png.png\" />\n",
+    "<img src=\"https://www.artandstick.be/getsupercustomizedimage.php5?objid=289&colorid1=2&colorid2=4&colorid3=4&colorid4=4&colorid5=4&way=NORMAL&transparent=Y\" />    \n",
+    "<!-- <img src=\"https://static.vecteezy.com/system/resources/previews/023/636/525/original/3d-rendering- -radiation-icons-sign-caution-danger-symbol-free-png.png\" /> -->\n",
     "<img src=\"https://thegraphicsfairy.com/wp-content/uploads/2016/07/Frog-Drawing-Green-3-GraphicsFairy.jpg\" />\n",
     "</div>"
    ]
@@ -66,7 +67,7 @@
     }
    },
    "source": [
-    "# Frösche, Wetten, Wetter und Wetterfrösche"
+    "# Frösche, Wetter und Wetterfrösche"
    ]
   },
   {
@@ -169,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "f8a3a4d9",
    "metadata": {
     "hideCode": false,
@@ -186,14 +187,14 @@
     "\n",
     "p = 0.6 # von l nach r\n",
     "q = 0.2 # von r nach l\n",
-    "N = 100_000 # Simulationen\n",
+    "N = 1_000_000 # Simulationen\n",
     "\n",
     "# Los geht's!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "41017b8e",
    "metadata": {
     "hideCode": false,
@@ -207,9 +208,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100000/100000\n",
-      "  0.2473 of the time at 0 (24728 total)\n",
-      "  0.7527 of the time at 1 (75272 total)\n"
+      "1000000/1000000\n",
+      "  0.2501 of the time at 0 (250098 total)\n",
+      "  0.7499 of the time at 1 (749902 total)\n"
      ]
     }
    ],
@@ -834,7 +835,7 @@
     "\\end{align*}\n",
     "$$\n",
     "\n",
-    "Wenn es heute regnet $X_0 = \\begin{pmatrix}1 & 0 & 0 \\end{pmatrix}$, ist die Wettervorschau für morgen $X_0 \\cdot M$, bzw für in einer Woche $X_0 \\cdot M \\cdot \\dots M = X_0 \\cdot M^{14}$."
+    "Wenn heute die Sonne scheint $X_0 = \\begin{pmatrix}1 & 0 & 0 \\end{pmatrix}$, ist die Wettervorschau für morgen $X_0 \\cdot M$, bzw für in zwei Wochen $X_{14} = X_0 \\cdot  M \\cdot \\dots M = X_0 \\cdot M^{14}$."
    ]
   },
   {
@@ -1006,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "id": "e9f7e2f6",
    "metadata": {
     "hideCode": false,
@@ -1019,36 +1020,52 @@
     {
      "data": {
       "text/plain": [
-       "array([0.40909225, 0.3484844 , 0.24242335])"
+       "[1, 0, 0]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#from sympy import symbols as S, Matrix as Mat\n",
     "from numpy import array\n",
     "\n",
-    "M = array([[0.6, 0.3, 0.1], [0.4, 0.3, 0.3], [0.1, 0.5, 0.4]])\n",
+    "M = array([\n",
+    "    [0.6, 0.3, 0.1], \n",
+    "    [0.4, 0.3, 0.3], \n",
+    "    [0.1, 0.5, 0.4]]\n",
+    ")\n",
+    "\n",
     "x0 = [1, 0, 0]\n",
-    "x0 @ M @ M @ M @ M @ M @ M @ M @ M @ M @ M @ M @ M @ M @ M"
+    "x0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "ae437f47",
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.40908903, 0.34848547, 0.2424255 ])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from numpy import linalg\n",
-    "x0 @ linalg.matrix_power(M, 14)"
+    "x0 = [0, 0, 1]\n",
+    "x0 @ linalg.matrix_power(M, 14) # schnelles Exponenzieren"
    ]
   },
   {
@@ -1056,7 +1073,7 @@
    "id": "6efbd936",
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -1089,20 +1106,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "9b76997c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9999999999999989\n",
-      "[0.40909091 0.34848485 0.24242424]\n",
-      "[0.40909091 0.34848485 0.24242424]\n"
-     ]
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# linalg.eig berechnet Rechts-Eigenvektoren von Matrizen, wir suchen allerdings Links-Eigenvektoren\n",
     "# Um zum richtigen Ergebnis zu kommen, müssen wir M also spiegeln (M.transpose())\n",
@@ -1140,7 +1151,7 @@
     "\n",
     "<div class=\"alert alert-info\">\n",
     "\n",
-    "Viele Leute werfen Münzen in den Teich der Frösche.\n",
+    "Viele Leute werfen **faire** Münzen in den Teich der Frösche.\n",
     "Wir sollen einen Frosch aussuchen, der der neue Schatzmeister werden soll. \n",
     "Wie können wir aus den 8 Fröschen **fair** einen auswählen?\n",
     "    \n",
@@ -1202,7 +1213,7 @@
     "\n",
     "<div class=\"alert alert-info\">\n",
     "\n",
-    "Viele Leute werfen Münzen in den Teich der Frösche.\n",
+    "Viele Leute werfen **faire** Münzen in den Teich der Frösche.\n",
     "Wir sollen einen Frosch aussuchen, der der neue Schatzmeister werden soll. \n",
     "Wie können wir aus den 7 Fröschen **fair** einen auswählen?\n",
     "    \n",
@@ -1246,7 +1257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "id": "88435c94",
    "metadata": {
     "hideCode": false,
@@ -1259,11 +1270,13 @@
     {
      "data": {
       "text/plain": [
-       "array([0. , 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n",
-       "       0. ])"
+       "array([4.90909347e-91, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.42857143e-01,\n",
+       "       1.42857143e-01, 1.42857143e-01, 1.42857143e-01, 1.42857143e-01,\n",
+       "       1.42857143e-01, 1.42857143e-01])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1288,7 +1301,7 @@
     "[ 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 1.0]\n",
     "])\n",
     "v = array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n",
-    "v @ M"
+    "v @ linalg.matrix_power(M, 3*100)"
    ]
   },
   {
@@ -1306,8 +1319,9 @@
     "\n",
     "Die Prozesse, die wir bisher gesehen haben lassen sich als *Markovketten* verstehen. Die Ketten, die wir betrachten teilen die folgenden Eigenschaften:\n",
     "\n",
-    "- Zeit vergeht für unsere Prozesse $X_1, X_2, \\dots$ **diskret**\n",
+    "- Zeit vergeht für unsere Prozesse $X_0, X_1, X_2, \\dots$ **diskret**\n",
     "- Wir können die Übergangswahrscheinlichkeiten in einer $n \\times n$ Matrix $M$ auflisten\n",
+    "- Markovketten sind gedächtnislos\n",
     "\n",
     "Außerdem ist $M$ eine *stochastische Matrix* und erfüllt\n",
     "\n",
@@ -1316,73 +1330,102 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1aa7ccc8",
+   "cell_type": "markdown",
+   "id": "3ca2367b",
    "metadata": {
-    "hideCode": false,
-    "hidePrompt": false,
     "slideshow": {
      "slide_type": "slide"
-    },
-    "tags": [
-     "hide_cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
+   },
+   "source": [
+    "# A: A simple Language Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9598a4eb",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "source": [
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import networkx as nx\n",
     "\n",
-    "seed = 13649  # Seed random number generators for reproducibility\n",
-    "G = nx.random_k_out_graph(10, 3, 0.5, seed=seed)\n",
-    "pos = nx.spring_layout(G, seed=seed)\n",
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "Mit dem `hp.txt`, generiere Harry Potter fanfiction! Ein brandneues Universum an Unsinn erwartet dich!\n",
+    "    \n",
+    "</div>\n",
     "\n",
-    "node_sizes = [3 + 10 * i for i in range(len(G))]\n",
-    "M = G.number_of_edges()\n",
-    "edge_colors = range(2, M + 2)\n",
-    "edge_alphas = [(5 + i) / (M + 4) for i in range(M)]\n",
-    "cmap = plt.cm.plasma\n",
+    "https://dominic.leafbla.de/hp_parsed.txt\n",
+    "https://dominic.leafbla.de/forschungstage\n",
     "\n",
-    "nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=\"indigo\")\n",
-    "edges = nx.draw_networkx_edges(\n",
-    "    G,\n",
-    "    pos,\n",
-    "    node_size=node_sizes,\n",
-    "    arrowstyle=\"->\",\n",
-    "    arrowsize=10,\n",
-    "    edge_color=edge_colors,\n",
-    "    edge_cmap=cmap,\n",
-    "    width=2,\n",
-    ") \n",
-    "# set alpha value for each edge\n",
-    "for i in range(M):\n",
-    "    edges[i].set_alpha(edge_alphas[i])\n",
+    "### Code Beispiele\n",
+    "https://dominic.leafbla.de/crawler.py (Crawlt Wiki)\n",
+    "https://dominic.leafbla.de/markov.py (Rechnet mit den Daten davon)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dd3d18c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# B: Monopoly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e436130d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
     "\n",
-    "pc = mpl.collections.PatchCollection(edges, cmap=cmap)\n",
-    "pc.set_array(edge_colors)\n",
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "Wo sollte ich in Monopoly versuchen meine Häuser zu bauen?\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea10341b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# C: Pagerank"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2faef6b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
     "\n",
-    "ax = plt.gca()\n",
-    "ax.set_axis_off()\n",
-    "plt.colorbar(pc, ax=ax)\n",
-    "plt.show()"
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "Crawle eine kleine Wiki, analysiere ihre Links und finde die *relevantesten* Seiten!\n",
+    "    \n",
+    "</div>"
    ]
   }
  ],
  "metadata": {
-  "celltoolbar": "Tags",
+  "celltoolbar": "Slideshow",
   "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",