From ccd8816d3b496556727e3eb1278c8c719ccebafe Mon Sep 17 00:00:00 2001
From: Kai-Philipp Nosper <kai-philipp.nosper@stud.h-da.de>
Date: Mon, 24 Jan 2022 21:39:21 +0100
Subject: [PATCH 1/5] Add headline to notebooks

---
 .../algorithm_comparison_classification.ipynb | 314 +++++++++++++-----
 notebooks/examples/autoencoder.ipynb          |   8 +
 .../data_analysis_and_preprocessing.ipynb     |   7 +
 notebooks/examples/k-means.ipynb              |   2 +-
 notebooks/examples/linear_regression.ipynb    |  10 +-
 .../examples/multi_layer_perceptron.ipynb     |   8 +
 notebooks/examples/random_forest.ipynb        | 101 +++---
 .../examples/recurrent_neural_network.ipynb   |   4 +-
 .../examples/single_layer_perceptron.ipynb    |   8 +
 9 files changed, 335 insertions(+), 127 deletions(-)

diff --git a/notebooks/examples/algorithm_comparison_classification.ipynb b/notebooks/examples/algorithm_comparison_classification.ipynb
index 54fd1b6..8aac974 100644
--- a/notebooks/examples/algorithm_comparison_classification.ipynb
+++ b/notebooks/examples/algorithm_comparison_classification.ipynb
@@ -1,8 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algorithm comparison for classification"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 220,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -18,116 +31,263 @@
     "from sklearn.svm import SVC\n",
     "from sklearn.metrics import accuracy_score\n",
     "from sklearn.impute import  SimpleImputer"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "data = pd.read_csv(\"../data/Titanic/titanic.csv\")\n",
-    "\n",
-    "data.head()\n"
-   ],
+   "execution_count": 221,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "execution_count": 221,
    "outputs": [
     {
      "data": {
-      "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
      },
      "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "data = pd.read_csv(\"../data/Titanic/titanic.csv\")\n",
+    "\n",
+    "data.head()\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 222,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "data[\"Sex\"] = data[\"Sex\"].map({\"male\":0, \"female\":1})\n",
     "\n",
     "features = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\"]\n",
     "target = [\"Survived\"]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "data.isnull().sum()"
-   ],
+   "execution_count": 223,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "execution_count": 223,
    "outputs": [
     {
      "data": {
-      "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
      },
      "execution_count": 223,
      "metadata": {},
      "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "data.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 224,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "imp = SimpleImputer(strategy=\"mean\")\n",
     "\n",
     "data[\"Age\"] = imp.fit_transform(data[[\"Age\"]])"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
-  },
-  {
-   "cell_type": "code",
+   },
+   "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.5, random_state=42)\n",
     "\n",
     "y_train = np.array(y_train).ravel()\n",
     "y_test = np.array(y_test).ravel()"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "execution_count": 225,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
    "outputs": [],
    "source": [
     "nearest_neighbors = KNeighborsClassifier()\n",
@@ -137,21 +297,23 @@
     "k_means = KMeans(n_clusters=3, init='k-means++')\n",
     "logistic_reg = LogisticRegression()\n",
     "support_vector = SVC()"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "SVC()"
+      "text/plain": [
+       "SVC()"
+      ]
      },
      "execution_count": 227,
      "metadata": {},
@@ -166,17 +328,17 @@
     "k_means.fit(X_train, y_train)\n",
     "logistic_reg.fit(X_train, y_train)\n",
     "support_vector.fit(X_train, y_train)"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
+   },
    "outputs": [],
    "source": [
     "nearest_neighbors_pred = nearest_neighbors.predict(X_test)\n",
@@ -186,17 +348,17 @@
     "k_means_pred = k_means.predict(X_test)\n",
     "logistic_reg_pred = logistic_reg.predict(X_test)\n",
     "support_vector_pred = support_vector.predict(X_test)"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
+   },
    "outputs": [],
    "source": [
     "nearest_neighbors_acc = accuracy_score(y_test, nearest_neighbors_pred)\n",
@@ -206,22 +368,24 @@
     "k_means_acc = accuracy_score(y_test, k_means_pred)\n",
     "logistic_reg_acc = accuracy_score(y_test, logistic_reg_pred)\n",
     "support_vector_acc = accuracy_score(y_test, support_vector_pred)"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -241,22 +405,16 @@
     "fig.autofmt_xdate()\n",
     "\n",
     "plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "This comparison shows that for this dataset using Random Forest gives us the best result. A comparison like this is however not necessarily ideal since different algorithms excel at different problems."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "This comparison shows that for this dataset using Random Forest gives us the best result. A comparison like this is however not necessarily ideal since different algorithms excel at different problems."
+   ]
   }
  ],
  "metadata": {
@@ -280,4 +438,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/notebooks/examples/autoencoder.ipynb b/notebooks/examples/autoencoder.ipynb
index 1114ebe..5f5b94c 100644
--- a/notebooks/examples/autoencoder.ipynb
+++ b/notebooks/examples/autoencoder.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "52b235c7",
+   "metadata": {},
+   "source": [
+    "# Autoencoder"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/notebooks/examples/data_analysis_and_preprocessing.ipynb b/notebooks/examples/data_analysis_and_preprocessing.ipynb
index 9cd77bb..2007de6 100644
--- a/notebooks/examples/data_analysis_and_preprocessing.ipynb
+++ b/notebooks/examples/data_analysis_and_preprocessing.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data analysis and preprocessing"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
diff --git a/notebooks/examples/k-means.ipynb b/notebooks/examples/k-means.ipynb
index 8f576c3..8a6d3c7 100644
--- a/notebooks/examples/k-means.ipynb
+++ b/notebooks/examples/k-means.ipynb
@@ -5,7 +5,7 @@
    "id": "0fa83e68",
    "metadata": {},
    "source": [
-    "# **Customer Segmentation with K-Means Clustering**\n",
+    "# Customer Segmentation with K-Means Clustering\n",
     "\n"
    ]
   },
diff --git a/notebooks/examples/linear_regression.ipynb b/notebooks/examples/linear_regression.ipynb
index 3b2372c..b621210 100644
--- a/notebooks/examples/linear_regression.ipynb
+++ b/notebooks/examples/linear_regression.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "18054401",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -380,4 +388,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/examples/multi_layer_perceptron.ipynb b/notebooks/examples/multi_layer_perceptron.ipynb
index 3b5862f..653f01e 100644
--- a/notebooks/examples/multi_layer_perceptron.ipynb
+++ b/notebooks/examples/multi_layer_perceptron.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "86975b4a",
+   "metadata": {},
+   "source": [
+    "# Multi Layer Perceptron"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/notebooks/examples/random_forest.ipynb b/notebooks/examples/random_forest.ipynb
index 385f489..b7be60f 100644
--- a/notebooks/examples/random_forest.ipynb
+++ b/notebooks/examples/random_forest.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random Forest"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -16,19 +23,25 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "As the random forest is closely related to the decision tree we will be using the same dataset."
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
-   }
+   },
+   "source": [
+    "As the random forest is closely related to the decision tree we will be using the same dataset."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# import data\n",
@@ -40,26 +53,26 @@
     "#define features and target\n",
     "features = [\"Age\", \"Sex\", \"BP\", \"Cholesterol\", \"Na_to_K\"]\n",
     "target = [\"Drug\"]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "As usual, we split the data into a training and testing set and define our model."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "As usual, we split the data into a training and testing set and define our model."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# split data\n",
@@ -69,26 +82,26 @@
     "X_test = pd.get_dummies(X_test)\n",
     "# define model\n",
     "model = RandomForestClassifier()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "Now we train the model using our training data"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "Now we train the model using our training data"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -100,7 +113,9 @@
     },
     {
      "data": {
-      "text/plain": "RandomForestClassifier()"
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
      },
      "execution_count": 15,
      "metadata": {},
@@ -110,29 +125,29 @@
    "source": [
     "#train\n",
     "model.fit(X_train, y_train)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "And now that we have our trained model we can check how well it performs."
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
-   }
+   },
+   "source": [
+    "And now that we have our trained model we can check how well it performs."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -148,13 +163,7 @@
     "# score the model\n",
     "acc=accuracy_score(y_true=y_test, y_pred=predictions)\n",
     "print(f\"Accuracy: {acc*100}%\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   }
  ],
  "metadata": {
@@ -178,4 +187,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/notebooks/examples/recurrent_neural_network.ipynb b/notebooks/examples/recurrent_neural_network.ipynb
index 38a7910..6a491b3 100644
--- a/notebooks/examples/recurrent_neural_network.ipynb
+++ b/notebooks/examples/recurrent_neural_network.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "id": "d421e819-8e21-4033-9e21-cdfbe6ff5b48",
    "metadata": {},
-   "source": []
+   "source": [
+    "# Recurrent Neural Network"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/notebooks/examples/single_layer_perceptron.ipynb b/notebooks/examples/single_layer_perceptron.ipynb
index 1acf65a..6822e11 100644
--- a/notebooks/examples/single_layer_perceptron.ipynb
+++ b/notebooks/examples/single_layer_perceptron.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7a974be2",
+   "metadata": {},
+   "source": [
+    "# Singler Layer Perceptron"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0565a4b8",
-- 
GitLab


From fdca066ed124ac2d49e0b99aab4e214162fdba7a Mon Sep 17 00:00:00 2001
From: Kai-Philipp Nosper <kai-philipp.nosper@stud.h-da.de>
Date: Mon, 24 Jan 2022 21:51:26 +0100
Subject: [PATCH 2/5] Add headline to exercise and solution

---
 notebooks/exercises/exercise_k-means.ipynb    |  3 +-
 .../exercise_linear_regression.ipynb          |  7 ++++
 .../exercise_logistic_regression.ipynb        |  3 +-
 .../exercise_multilayer_perceptron.ipynb      |  7 ++++
 .../exercises/exercise_random_forest.ipynb    |  7 ++++
 .../exercise_recurrent_neural_network.ipynb   |  7 ++++
 .../exercise_single_layer_perceptron.ipynb    |  2 +-
 notebooks/exercises/solution_k-means.ipynb    | 13 ++++---
 .../solution_linear_regression.ipynb          |  9 ++++-
 .../solution_logistic_regression.ipynb        | 38 +++++++++++--------
 .../solution_multilayer_perceptron.ipynb      | 10 ++++-
 .../exercises/solution_random_forest.ipynb    |  7 ++++
 .../solution_recurrent_neural_network.ipynb   |  9 ++++-
 .../solution_single_layer_perceptron.ipynb    |  9 ++++-
 14 files changed, 102 insertions(+), 29 deletions(-)

diff --git a/notebooks/exercises/exercise_k-means.ipynb b/notebooks/exercises/exercise_k-means.ipynb
index e642cd9..5a32d00 100644
--- a/notebooks/exercises/exercise_k-means.ipynb
+++ b/notebooks/exercises/exercise_k-means.ipynb
@@ -5,7 +5,8 @@
    "id": "360252d2-aaac-4ba8-aec7-1e59ea001108",
    "metadata": {},
    "source": [
-    "# Miles per Gallon Clustering for Cars"
+    "# K-Means\n",
+    "Miles per Gallon Clustering for Cars"
    ]
   },
   {
diff --git a/notebooks/exercises/exercise_linear_regression.ipynb b/notebooks/exercises/exercise_linear_regression.ipynb
index f99b715..245d174 100644
--- a/notebooks/exercises/exercise_linear_regression.ipynb
+++ b/notebooks/exercises/exercise_linear_regression.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/exercises/exercise_logistic_regression.ipynb b/notebooks/exercises/exercise_logistic_regression.ipynb
index 9caa945..d18365b 100644
--- a/notebooks/exercises/exercise_logistic_regression.ipynb
+++ b/notebooks/exercises/exercise_logistic_regression.ipynb
@@ -5,7 +5,8 @@
    "id": "9cb073bf-96dd-43af-8802-0fc4d9b4a0db",
    "metadata": {},
    "source": [
-    "# Logistic Regression Exercise\n",
+    "# Logistic Regression\n",
+    "\n",
     "Your task will be to use logistic regression make a predictive analysis on the titanic dataset from kaggle.\n",
     "We want to make a predictive analysis if the passenger survived or not. Keep this in mind!\n",
     "\n",
diff --git a/notebooks/exercises/exercise_multilayer_perceptron.ipynb b/notebooks/exercises/exercise_multilayer_perceptron.ipynb
index e259edd..fcc3481 100644
--- a/notebooks/exercises/exercise_multilayer_perceptron.ipynb
+++ b/notebooks/exercises/exercise_multilayer_perceptron.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilayer Perceptron"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/exercises/exercise_random_forest.ipynb b/notebooks/exercises/exercise_random_forest.ipynb
index 270474f..7d5782a 100644
--- a/notebooks/exercises/exercise_random_forest.ipynb
+++ b/notebooks/exercises/exercise_random_forest.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random Forest"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/notebooks/exercises/exercise_recurrent_neural_network.ipynb b/notebooks/exercises/exercise_recurrent_neural_network.ipynb
index 7ea7ab9..9639d05 100644
--- a/notebooks/exercises/exercise_recurrent_neural_network.ipynb
+++ b/notebooks/exercises/exercise_recurrent_neural_network.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recurrent Neural Network"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/exercises/exercise_single_layer_perceptron.ipynb b/notebooks/exercises/exercise_single_layer_perceptron.ipynb
index b64930e..58628c8 100644
--- a/notebooks/exercises/exercise_single_layer_perceptron.ipynb
+++ b/notebooks/exercises/exercise_single_layer_perceptron.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Single Layer Perceptron Execise\n",
+    "# Single Layer Perceptron\n",
     "\n",
     "In this execise your task is to implement an AND-Gate as Single Layer Perceptron.\n",
     "You can start at your own or follow the following tasks.\n",
diff --git a/notebooks/exercises/solution_k-means.ipynb b/notebooks/exercises/solution_k-means.ipynb
index 97d260b..de5a314 100644
--- a/notebooks/exercises/solution_k-means.ipynb
+++ b/notebooks/exercises/solution_k-means.ipynb
@@ -5,7 +5,8 @@
    "id": "360252d2-aaac-4ba8-aec7-1e59ea001108",
    "metadata": {},
    "source": [
-    "# Miles per Gallon Clustering for Cars"
+    "# K-Means\n",
+    "Miles per Gallon Clustering for Cars"
    ]
   },
   {
@@ -478,7 +479,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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LnU6HsLAwAEBwcDC++eYbAMDs2bNx6dIlAICHhwfOnj0LoPrxomyejLHamT9/Pj7++GMAwOrVq4VF6H79+mHXrl0YNGgQkpOTkZGRgYiICNjZ2QEoP7efMmUKcnNzkZqaik2bNuHbb78Vnt/f3x9JSUm4dOmSMM8ICwuDn58fVCoVz9cZq6Po6GicP38eer0eU6dOBQC88847yMjIgFqtxsmTJwEAdnZ22LZtG7RaLTQaDUaMGAEAVfbtsqraxmAwICwsDBkZGRg1ahQ++ugjpKWlQa1W48CBA2jSpEmlc++y7zf69++PzMxMaLVabN26FTY2NsJzL1myBBkZGdBqtcJ7FcYaspqsoQOAk5MToqOjodFocO7cObz++usAAGdnZ8TGxkKv12Pz5s3lPiRe1XvqykyePBlr1qwRfv/oo4+wevXqap/nP//5D9LT06HX64W1POD5caQhIw7LDzc3N3r27Bl169aNZDIZnT9/nrZu3UoAaOjQoRQdHU2hoaGkVqvJ1taWXFxcqKCggFq3bk0jR46kY8eOkUwmo5YtW9Kvv/5KI0eOFH2fODgaQnTt2pVyc3PJxcWFAJCTkxNt27aNhg0bRgBo6tSp9PXXXxMACg8PF/qmUqkkb29vAkBERO+99x4BoJUrV9I//vEPAkCHDx+m8ePHEwD661//Sg8fPiQANGjQIPr+++8JAMlkMoqJiSE/Pz9yc3OjoqIi6tWrFwEgLy8viouLE3J1dHQU/fXi4JBaeHl5kVarpSZNmpCDgwPl5+fTvHnzhP7q7OxMOTk5wval/Sg8PJyOHz9OMpmMXnvtNbp69So1btyYAgICKCYmhgCQg4MDyeVyAkADBgygAwcOEACaPn06RUZGCvc5OTmRtbU1JSUlUfPmzQkAjR49WpgHKJVKCgsLIwA0e/Zsun79OrVq1YpsbGzo6tWr5OzsTJ07d6YjR46QtbU1AaD169cL40dVY0zZMYmDo6FF6dy7e/fuBID2799P48aNI41GQ/7+/gSAli5dSmvWrCGgpB+uX79eeLxWq6U2bdoQ8L9xYerUqUL/srGxofT0dHJ3d6eAgAB6/PgxeXh4kJWVFcXFxdHIkSOpdevWdOXKFWrevDnJ5XL66aefaNiwYdSyZUtKS0sjABQZGUlpaWnUpk0bmjBhAn355ZcvHC/K5snBwVG76NWrF0VERBAASkhIoNTUVLK2tqYvvviCFixYQGfOnKGmTZsSAFqwYAEtXryYgP/N7Vu3bk0Gg0Go7QkJCfTtt98SUFJ3IyIiSCaTUZcuXSg/P58AlJs7cHBw1C2cnJwIANna2pJOp6MWLVpQQUEBubu7l7s/LCxMqO0ASKFQkIuLywv7dnXbGAwG+vTTT4XndHZ2Fn5etmwZzZo1i4Dn596lvzdu3JgKCgrI09OTANCOHTtozpw5wnOXPn7GjBm0efNm0V9rDg6xoyZr6ABo3bp19MUXXxAA6tevH6lUKgJAa9euFfrvu+++S0RELi4u1b6nNhgMwrpfadjZ2dHFixeF7ZOSkqhbt27VPk/pWGRlZUVKpZJef/114fnLjiMNNcp/RYBZNIPBAL1eDwC4cOGC8OkxnU4Hd3d3qNVqHD58GE+ePMGTJ0+gVCrh4+ODvn37IjIyEkSE27dvQ6lUirkbjDUo/fv3R2RkJH755RcAwP3797FlyxYsWLAAhw8fxuTJk4VPlFXl6dOnOHr0KAAgIyMDgwYNAgD06dNH+ITXDz/8gJUrVwIAAgMDERgYCJVKBQCwt7eHp6cnCgoKcOXKFaSmpgIALl++jPbt22PdunU4duwY4uLijP8CMGbh/Pz8EB0djcePHwMAjhw5Uu7+wsJCPHnyBFu3bsXRo0eFvgoAERERICJcvHgRly9fRufOncs91tHRETt27ICnpyeICI0aNQIADBw4EBs3bkRRURGAknHjD3/4A7p164b4+HgAJd80uXnzpvBcpXnpdDpcuHABt27dAlDSz11dXdG3b194e3sjPT0dANCkSRPcuXMHQNVjDGMNncFggEajAVDSNzp06ACFQoGEhAQAwI4dOxAZGSlsv3//fuHnpKQkbN++HREREYiKigJQUp//+Mc/Ijg4GEDJGODp6YnffvsNaWlpMBgMAIC9e/eib9++ePbsGU6fPo179+4BAHbv3g1/f38cPnwY9vb2sLe3h6urK/bs2QN/f3/4+fkhKioKnTp1qna8KJsnY6x2MjIy4O3tDQcHBzx9+hSZmZl488034efnhyNHjqBr165ISkoCANjY2ODcuXPlHu/j44MzZ87g/v37AIDIyEjhG90AcOjQIRARsrOz0bJlS/PtGGP13OzZs/H+++8DAFxdXTFt2jQkJCTg559/BgChTw4cOBAhISHC4x48eIAhQ4a8sG/7+vpWu03Z2tutWzcsX74cCoUC9vb2iI2NrTb3Tp06wWAwID8/H0DJ/GPmzJlYu3YtAAjzjIyMDOEbaIw1dC9aQweAvn37CmtqSqUSLi4ucHBwgL+/v9CXfvzxR/z6668AgAEDBlT5nroyjx49wqlTp/Dee+8hOzsbjRo1gl6vx8yZM6t8ntGjR2PatGmwtrZG69at0bVrV+G6wTyHB/hAVz3y9OlT4efi4mLh9+LiYuG0Z0RU7jEVf2eMiS85ORnu7u4ICAiAXC7HhQsXqt3+2bNnws9FRUXlTnNYWR+XyWRYsWIFNm3aVO52Nzc3PHr0SPj9wYMH6N69O9555x1Mnz4do0ePxocffljX3WKsQSoqKoKPjw8GDBiA4OBgzJo1S7ho9Itq8rJly6BUKjFixAi4ubnh9OnTVf4dmUyGCxcu4K233qr0/rJzgorzBWtra8hkMuzYsQOLFi167rHVjTGMNWRl+1JRUZFwmsKqlK2xM2bMgI+PD4YMGSIsjMtkMnz88cfPfbAkICCg1nP45ORkTJ48WTit6pQpU9C7d2/MmzcP7dq1q3a8KJsnY6x2fv/9dxgMBkyaNEk4hXi/fv3w2muvwWAwID4+/qWup1V23OHr6TJmHAEBARg4cCB69+6Nx48fQ6lUQq1WP/chtKrIZLIX9u0XbVO29m7fvh3Dhw+HVqvFxIkT8fbbb9dqfyoqHTd4Hs/Y/9RkDb22qntPXZUtW7Zg0aJFyMnJQXh4eLXP4+7ujvnz56Nnz5548OABwsPDYWtrK9zPc3i+RleDM2zYMDRu3BjOzs54++23kZ6ejqSkJIwcORIymQwtWrR46SLKGKu5U6dOYdSoUXB2dgZQcg5gANi5cyf27NkjFLq6SEpKEj5tNm7cOOH22NhYTJkyRTgneJs2bfDKK68893gXFxdYWVkhKioKn3/+Oby8vOqcC2P1VUJCAoYPHw5bW1vY29vjT3/6U7n77ezs4OjoiOPHj+OTTz5B9+7dhftGjRoFmUyG9u3bo3379sjNzS33WEdHR1y/fh0AMGnSJOH2+Ph4/OUvf4FcLgdQMm7k5ubilVdega+vLwDA2tq63MWqX+Snn35CcHCwMBY4OTmhXbt21T7m4cOHcHBwqPHfYKy+KywsxP3799G3b18AJdfgLL3+VkXt27dHWloaQkNDcffuXbi6uiI2NhYzZswQ3lx7enqiadOmAEq+5eHu7g6ZTIYxY8bg7NmzSEtLQ0BAgFCvx44dK/y9xMREzJ8/HwkJCVCpVOjXrx+ePn2K//73vy89XjDGqle2/yUmJmL69OlQqVRISUlBnz590KFDBwBA06ZN4enpWe6x6enpCAgIgEKhgFwur9G1s7keM/ZyHB0dcf/+fTx+/BidOnWCr68vbG1t4e/vL3yzo/R9enx8PGbOnCk8VqFQ1Khv12SbUg4ODrh58yasra3LvY+vqq/n5ubC3d1deO7q5h+MsZpLTEwU+mBAQADu3buHhw8fIiEhQThoPXjwYGE9ry7vqdPS0uDq6ooPPvgAe/furfZ5mjVrhkePHqGwsBAtWrRAUFCQSfbbkvGBrgZGq9VCqVQiJSUFy5Ytw82bN3Hw4EFcu3YNWVlZ2LVrFzIzM1FYWCh2qow1CFlZWfjXv/6FM2fOQK1WCxee3L17N5ycnIRCVxdz5szBzJkzodVq0bZtW+H2+Ph47NmzB+fOnYNWq8WBAwcqnTC3bdsWp0+fhkqlwq5du/D3v/+9zrkwVl+pVCrs378fGo0Gx48fF04vUMrBwQFHjx6FRqPB2bNn8be//U24r6CgAGlpaTh+/DimT59e7lNlAPDVV19hxYoVyMzMLPepsi1btqCgoABarRZqtRoffPABnj17huDgYKxcuRJqtRpqtbrKb2tUJjs7G59//jni4uKg0WgQHx+P1q1bV/uYffv24dNPP0VmZibat29f47/FWH02ceJErFq1ChqNBm+88Qb++c9/VrrdqlWroNVqodPpkJycDI1Ggy1btiArKwuZmZnQ6XT4/vvvhb6fnp6O7777DtnZ2TAYDIiOjsatW7ewcOFCKJVKaDQaZGRkCKcpTUxMRLt27ZCQkIDi4mJcvXoVZ8+eBYCXHi8YY9VLTExE69atce7cOdy5cwdPnjxBYmIi7t27h0mTJmHv3r3Che0rfmPkxo0b+PLLL5GWloakpCT8/PPPL3xvrtVqUVRUBLVajblz55pwzxirn06cOAFra2tkZWUhLCwMKSkpuHv3LqZNm4aoqCio1WrhlGDLly+Hk5MTdDod1Go1+vXrV6O+XZNtSi1evBipqalISkpCTk6OcHtVc++nT59i8uTJiIyMhFarRXFxMTZu3GiCV4qxhmXJkiXw9vaGRqNBWFgYJk6cCABYunQp/P39odfrMWLECFy5cgVA3d5TAyWXNEhKSsKDBw+qfR6tVguVSoWcnBzs2bNHOBUqK0/0C4VxmCdCQ0Np3rx5ld5nZ2dHQMlFLy9evEgtW7YUPV8OjoYcI0eOpJ07d4qeBwcHh2mi4sWkOTg4OKqKgIAAiomJET0PDg4O80Tpe3O5XE5Hjhyh4cOHi54TBwcHBwcHh2k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",
       "text/plain": [
        "<Figure size 2160x504 with 7 Axes>"
       ]
@@ -515,7 +516,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1440x1080 with 2 Axes>"
       ]
@@ -6717,7 +6718,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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PJE8+Wc4tts027dsXLy73BQAAAAB0DcIsANabWq309Kr7n/9Z9fInn9wY4vCzn+14mYEDy7m9Bgwo05IlJczaeOOO1/n5z5Pf/jbZYovkl79s337WWcnllyc77JD84AeNgGvJknL5q18lzc3Jdtslxx3XPgi7+upkxowSyo0b13bdxYvL+cQWLCj1bbFF+/VffLHzwjTDNAIAAADQHQmzAOiypk9vDFV4zDFlaMEVzZqVHH10+/nz5yfveU8j5KpPTz1V2p95JvnmN9u29e+f3HVXaV+0KLnxxvbrL15c2jfaKNlxx8Z69fY77ihh1siRJYxb0cknl2XGjk2++tX27SedVB7zwQcnn/hE+7Dr618vj3nffZODDmrf/utfl9p32y3ZddfG/F13TQ47rNSYlG35+c+XoPCGG0rgtmRJCdoAAAAAoCtpStJlBlOaOHFiRo8eXXUZAHRBBx5YzpHVepjCF15Ivv/9rtm7qF+/ch6xFcOwRx5Jnnuu9I7abbf27ddcU84htuee5TGv2P7975dziB1ySHLkkW2DtAEDSrA3f37pNfbhD7+8mltayn0mySmnlLCsHnItXlyCrpNOKu0f/Wjy6lc32pcuLXX913+V9re/vQwhWV93yZLyuOrP1WtfW3qntW5fuLBxbrYhQ0oPtXr70qXVD/+oZxsAAABA51pZTqRnFgDdQj006C5hwtKlpffXysyeXaaVufPOMq3MX/9appW54ILksssaIdcFF7Q/51hSAqIzziihWN++jfm33lp6eNV7nvXvXx5T6/X69i3nNKu3DxnSaN9vv2TMmBLq1T3wQOP5OvHEZI892j/m8ePL9e9/v/Rua625OfniF8v1H/84GTy4EaYtWVJq/u//Lu1f+EKjJ129/a67kuuvL+3ve18J71qHaQ8/XGpsaiphYusgb/ToUnM9TB0xooSrSdfdB7saYSAAAACwpvTMAoBe4KKLOh6m8cknOx6mcV1pamqEXX36lN5XSem1tckmjbb+/cv51OoB3pveVMKxes+z/v1LrVdfXdo/8YnS3jpsu/POEtolyU9+kmy6aaNtwIASnJx5Zmmvh1qtXXppWW/gwOSqq1b/MS5dWqZzzy0B4pZbJj/6UbJsWZm/bFmZzj8/+cc/km22KUM8tm5burSse8cdybbbluek9fpLlzbOx7b11slb3tK+/eabkzlzyv3vvnvbtmXLkmnTSq/AQYPKMivWN3t2uaxv09a1rW2vuO7Ws7IrEgYCAADQG+iZBQC92LnndhwmnHtu595vrdY4b1drM2euer0bblh1+89+tur2f/u3VbcfemjbIKx//9ITLSm1nnpq2/avfGXlPdsuvrj0Urv//sb6t9xS5vXrVy779i1BUlJCvVqt3H7rZTbcsLRvtlny+te3Xbdv32Ty5BJmbb99CTVWdPLJJczac89S74rq52N74xvL41vRsceWYR6POCL55Cfbti1bVoa1nDu3BG1HHtk+TPv4x8s+9d73luCldfueeyYbbND2NgcOLHXsvXcJMn/0ozL/bW9LXvnKsl5LS5mefbZs56QEnVtv3bZ9wYJGsDNmTOm119LSWGbBgmTSpNK+++5lW9fXbWkpIeuMGaV9u+3Ktq+3LVtW6ps3r7QPGlQuW7fXA83OsmIYqGfgmhEIrh3bDwAAqJKeWQDQS/gics1V1bOtI01N7YOyvn1L4LNkSbLRRskWW7Rf5v77S9i05ZbJTjs15teXufHGErjtskvymte0b7/oorL+uHGNISRbt3/3u+X+3/GOEji1bttjj5WHgU89VW73Ix8p877whWT//Uvo16dPWX/u3BKgJcl3vlNqaO2RRxrrn3VWqb+1e+5pBHRnn53svHPb9ttvbwR8v/516T3X2k03NQLCyy5LNt+8bfv//E/yf/9vuf6Xv5SgsnXYduWVpddfU1NyySXlca/Y/tvflrDqrLPaBm3LliU77NAI0Vp74YUy/OZf/5pMmJAMHVpCxVqt7W1cc03pubjllo0hNutTrZb87W/Jgw+W9oMOatRXv50bbyz7+ogR5blvvW5LSwlv580r6++2W9v2ZcuSqVNLWDxsWPKKV7S97ZaW5L77yr4zZEjZtq3XrdVK+N3SUs6zt+GGbdet1UpYmZT9rR4Wt25P9A5cW7bf2vMevHZsv7Vj+60d22/t2H5rx/ZbO7bf2rH9qEolPbMOPvjgnHXWWenbt2/OPffcfPe73+3MuwMAVuG663zwXFNV9WzrSK226p5Azz1Xzv+1MrNmlWllpk8v08pMmFCmlbnyyjK1trIwcNas9mHgGWeUaWW++tVGaFGfWvvGN8rz1Lp9yZJG+3e/WwKRvn1LW1NTCQLrzjqrbXufPqXHW90555Tbb93+yCNtH2v9HHT1MO6uu0pbU1PZdq1r69u3BHp1c+Y05teX2WyzjrfFBhuUcKjePnBgCQ779i33Vb+dKVNKmDVoUHLIIW3vv0+fEiY9+GAJ8Y4/vv39PPxwCbN22in57Gfbt3/60yXMes1rki9/uX37CSeU89Htt1/pQbiiD36wBFYHH1x6EK7o3e8u5yD8wAdKD8IVHXxw6RF50kklrGtt2bLkrW8tNbQ+fuvb60tfarwufv7zyT77lGOsPs2Z0whCv/CF5LWvbdv++OPlNpJy+cpXNsK0Wi156KHk9NMb7dts03b9e+8tQWe9ffPNG20tLcnddzeGT/3iF0tY3Xr9O+5ILr+8UV/fvm3bJ01Krr227A+f/Wy5zaRR3223Jf/6Vwlgjz++7botLeVchJMnl5C0o+33yU+W7bfZZsnb317m19dNkokTS6/HIUOSN7+5ES7W7+PWW8tzP2xYCUpb15aU+ubMKQH9a17Ttr7641uwoLTvskvb267Vyr7/3HPlC5jtt29727VaOTYXLy7rjxjR/vanTy/70LBhbZ+b+m08+GC5PmRI2Qb1x13fBvVeyO98Z/KpTzV6qK7Yu3LTTctz0Lq2ZcsaQe3GG5fXvdZDvi5b1uhRvOGGbV8L6+u/+GL5e8CARtBbv42Wlsb7SP38lq3bW1+vmt6pa8f2Wzu239qx/daO7bd2bL+1Y/utPWHgutdpYVafPn3y4x//OAcddFAee+yxTJw4MVdccUWmTZvWWXcJANAp6h84fRBdM+syDHypIf2efnrV6z/wwKrbJ05cdftf/7rq9nrw0JGWluQHP1h5+wsvJP/f/9d+/qrCwOOOa/z9+OPJhz608tufPj1517tW3n777eUfrtZhWJ8+jS/Eb7klec97GiFgfbm5c0v7v/6VfOxjbddtakoee6y0//OfJdxpvW6fPo31//nPsmzrdfv0aXxhf9NN5TG3DuKamhr7w803l9tqfdt1w4d3/JhbL3PXXSX4bGpqTK2DzhkzGrddn1oHnbNnl328ft9NTcn8+Y32558vwUrr2urhR1ICh9brJyXEaP0YNtusbfsTTzTad9ut3Ebr+urbtk+fEibWr9fbFyxohFn1faP17T/3XAmzhg3rePvVeyluvnnHQeT3vle224gRJfRc0Te+UQKf7bcvYeKKTjutbONdd+14+NTx40tQO2pUI1RsrR6k7rtv8pnPtG+vB6lvfWsJ7FZUD1IPP7zjY6sepH7oQ+XYaK0epCZl6NuOhlo94YTyPnLKKSXsa23WrOSoo8r1//N/GmFf3YwZ5XhLSkj/6le3bb/77uTf/71c/8lPShjd2m23Nb6Q+tWvyvCtrd14Y7nfJPnDH8rwra1dfXUjqK33SG0dgP3pT2X42D59SnvStv3SS5Nf/KLs4/VhZFuHZxdcUHqyDhtWQvIVf7hQ337TpzfOh9k6iPvpT0uv0513bvScbd1+5pnlxwV77tl43W3dfsYZJSx+/esbIXzr9m9/u9z3fvslJ57Yvv7/83/K0L1veUtj32nd/qUvlR8yHHJIY99p3f65z5UhcA87rCzTWq1WalqypAztu+K+s2xZo+ajj04++tHy/HS0/bbZpoT0rc2bVx5fUpapB8X1+549u/Fe9olPlOO3tUcfbYT048c33r/qj++BB8pzn5SQfciQto992rTSWzkprwsbbdT2/qdMaYT4X/pSeV1u7dZby3t1nz7lRwCt7zspz/v115dtUH9daN1+443l/WTTTcvr2lve0nGY//GPt9129du49tryujRiROMYbt3+l7+UH5Fst10Z2nlFV1xRfkQycmQZFnvF9f/wh/Lav8su5T17xfZLLy2v/XvskbzhDW3b6sNjL1hQal/xB++1WnLhheXzyN57Nx5f6+1z/vllHxs7trzvtG5vaWl8Dtp33/IDj/e/v+Pt96lPtf9s89xz5fElZbtvtVXb2ubPb7yevPWt7d+b5s4t2z8pr8/11616fbNmlfPXJuUHGK3fY5PyOepf/yrX3/Wuxut2ff1HHml8TjziiMa+13rfvuOO8j7a+rmtt993X/ms0b9/GclgxfZp08rryoYblp7ySfmhSUfb79OfbntsJOX9+tFHy+OuP/et3XZb2XeGDSvP34puuaW8Lm25ZXn+V3TzzeX1oaPXjaR8jluwoPzQao892rf//e/ls9DIkW1fV+r+9rfynvrKV7Z/z0rKc9vSkrzqVeU+WqvVGs/9Hns03tP+/d9Xvv+1HjXihRdK/Un5TDF0aNt1nn228WO+vfduP2LCggXltScpx9Umm7RtnzevPD9JGWFixZrmzGmcO3rffcs+0tqsWWWkiaQ8tyu+J86cWUbhaGrq+Ll/9NHyuaFfv46f+0ceKctssEF536tb2fb7939vDKGflH1/9uzyuPfcs/3t33dfOT4326zxutHaPfeU43vIkPYjaCTlM82zz5Z9d8cd27ffeWfZt4YPb79vJGWUiBdfLK85K47AkZTjdunSst909D/X5Mll39t22/IDqNZqtcZzu912bUfw2GuvMrrIyn7MxJrptDBrzJgxuf/++/PQQw8lSX7729/m8MMPF2YBAN2Snm1rThi4dtZnz8D68Hw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       "text/plain": [
        "<Figure size 2160x504 with 1 Axes>"
       ]
@@ -9364,7 +9365,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2160x504 with 1 Axes>"
       ]
@@ -12011,7 +12012,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2160x504 with 1 Axes>"
       ]
diff --git a/notebooks/exercises/solution_linear_regression.ipynb b/notebooks/exercises/solution_linear_regression.ipynb
index a1a8e9f..f0d602d 100644
--- a/notebooks/exercises/solution_linear_regression.ipynb
+++ b/notebooks/exercises/solution_linear_regression.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 66,
@@ -428,7 +435,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/notebooks/exercises/solution_logistic_regression.ipynb b/notebooks/exercises/solution_logistic_regression.ipynb
index 67fe609..777d306 100644
--- a/notebooks/exercises/solution_logistic_regression.ipynb
+++ b/notebooks/exercises/solution_logistic_regression.ipynb
@@ -1,11 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4e16e30d",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9cb073bf-96dd-43af-8802-0fc4d9b4a0db",
    "metadata": {},
    "source": [
-    "# Task 1"
+    "## Task 1"
    ]
   },
   {
@@ -30,8 +38,8 @@
    "id": "0fe82155-f745-49e2-8c61-20fb651fedb0",
    "metadata": {},
    "source": [
-    "# Task 2\n",
-    "## Load Training Data"
+    "## Task 2\n",
+    "### Load Training Data"
    ]
   },
   {
@@ -268,8 +276,8 @@
     "tags": []
    },
    "source": [
-    "# Task 3\n",
-    "## Cleanup data"
+    "## Task 3\n",
+    "### Cleanup data"
    ]
   },
   {
@@ -332,7 +340,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -683,8 +691,8 @@
    "id": "b6b3ac8d-3b36-43ae-9740-e02d8b30fca4",
    "metadata": {},
    "source": [
-    "# Task 4\n",
-    "## Training"
+    "## Task 4\n",
+    "### Training"
    ]
   },
   {
@@ -1169,8 +1177,8 @@
    "id": "b993eec9-af95-4278-9e47-00b9c4bce97a",
    "metadata": {},
    "source": [
-    "# Task 5\n",
-    "## Predict"
+    "## Task 5\n",
+    "### Predict"
    ]
   },
   {
@@ -1201,7 +1209,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1252,8 +1260,8 @@
    "id": "bac9dd07-78d5-432b-a848-d9197aa84149",
    "metadata": {},
    "source": [
-    "# Task 6 (optional)\n",
-    "## Add new feature\n",
+    "## Task 6 (optional)\n",
+    "### Add new feature\n",
     "We want to add a new feature so that we can see the relation between survival and the amount of relatives on board.\n",
     "SibSp stands for siblings and Parch are the amount of parents or children."
    ]
@@ -1396,7 +1404,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1440x576 with 1 Axes>"
       ]
@@ -1517,7 +1525,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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LR0BQII6oKIrVepCLJ8MSfOtSpR4Oh4OIyEiio6OJiIggZ47s8fJ8+vlEXniuE+kCAmLSAgPT4+fepi7yWmTMGK2fn52IiAiuRV3DZrcRHR3NlBkz6dq5U8rckEWJeH74mgZuLzPG8PLCn3hz0wpqvfCsx9edP36CxaPH8t7h3bx/4n9EXLjI3kVL4+Sp2KIp+5asiHnT8o+RY3h58TwqNGvExpnf03hwf35994PkvB2VzHLlzMnznZ6mXqMnqPVoYzJmzEitB2vEybN771+cPBlG3dq14l2/fecumrRuxxNPPsWwQf3x8/OjWaOGLFm+kue696L7888yY85cmjdpTGBg+pS6LUvStUpUjNG1Hue9KnUY16g1dXu+QLHaNT26LihrVio0b8xbhcvTP08JAjIEUb1juzh5bn4dfu/iZYys+jCfP9GOis2bsOu3heQqUYxu303l6dDP8A8MTNZ7U3fvwsWLLFm+giXzf2LVwt8IDw9n3q+/x5x3Op2M+ugT+r/6coLXVyxfjl/nzub7b79h4qQpREZGkilTRkLHfswPM6ZSpnQplq1cxeMN6vPWOyPo89oAtm7fkVK3Zyli8/zwtVTQhLTt/HHXDt6XTp9h24/zKVy9ikfXlWpQl/8OHOLymf9wRkez9YdfKFrzgZjzGe4LplD1Kuz89Y941/oHBvLgs0+xfHwoTYcN5JvO3dm/eh0PdGybPDelks2a9RvIlycPwcGuXd8fq18vTmC9cuUqf//zD5269qB+4+Zs27mLHn1fi3lAeV3RIoUJCgrk75uGxT4P/ZruXZ/j1wULqRJSkVHvDmXcxC9T5N6sxkoPJzVwe1FAUBDp3IvcBwQFUfqx+hzb5dmiUGcPH6VwjWoxveRSjzzMib37Ys5XbtOCnfMXEJ3ADITHXn+ZZZ99gTM6moDA9BhjME4nAUFByXBXKjnluf9+tu/cRXh4BMYY1m7YSNFYu75nypSR9csWsfS3eSz9bR4h5csx4ZPRlC9bhiPHjhEdHQ3AseMn+PfAIfLmubEk8MFDhzl56hQPVK1CeEQEYrMhCBERCc9auddZaahEZ5V4UeZcOen+43TA9aBx44zv2PPHYkJaNKXd2A/JmCM7vX79jiPbdjK2YUuy5L6fZ74ax7gmbTi4YRNbvp/HoC2rcERHc2TrDlaH3pjWV619axaM+jhenVly30+h6lX49R3XOl7Lxk7kzY3LCT9/gQktOqTMjSuPVSxfjscbPELLp57Bz26ndKmStGvdkk8/n0i5MqV5pG78Bcau27x1O19Ods0qsdlsvD3wDYKzZY05//H4CbzSswcATRs+Rs9XXufLyVPo0+NFb9+WJSVXPBaR/MBUIBdggFBjzKciEgzMBgoBB4G2xphz4vpN8CnQGLgKPGuM2ZJkHd565T0Z6CvvKh595V0lKBleeT9UvqTHwbDgzn2J1iciuYHcxpgtIpIJ2Ay0AJ4FzhpjRrmXt85mjOkvIo2B3rgC9wPAp8aYBxIu3UWHSpRSiuSbDmiMOXG9x2yMuQTsxbVmU3NgijvbFFzBHHf6VOOyDsjqDv6J0qESpZTCO2uViEghoBKwHshljDnhPnUS11AKuIL6kViXHXWnnSAR2uNWSinAZvP8iL1bl/vodnN5IpIRmAv0NcZcjH3OuMao73icWnvcSinF7W2kcNNuXfHLEvHHFbSnG2N+cCeHiUhuY8wJ91DIKXf6MSB/rMvzudMSpT1upZQi+ca43bNEvgb2GmPGxDr1M9DZ/bkzMC9WeidxqQFciDWkkiDtcSulFMm6yNRDwDPAThHZ5k4biGuv3Tki0gXXto7X34j7DdeMkv24pgM+d6sKNHArpRTJN4/bGLOaxDeDfySB/AboeTt1aOBWSil0BxyllLKc1LAGiac0cCulFKljnW1PaeBWSil0qEQppSwnNayz7SkN3Eophfa4lVLKeuzW6XJr4FZKKbTHrZRS1qPTAZVSymK0x62UUtZyO6sD+poGbqWUAu1xK6WU1YjOKlFKKYvRoRKllLIWnQ6olFJWoz1upZSyGO1xK6WUtYhdA7dSSllKmpjHLSJjAZPYeWNMH6+0SCmlfCGNDJVsSrFWKKWUr6WFHrcxZkpKNkQppXwpTU0HFJEcQH+gDJD+eroxpr4X26WUUinLQj1uT97xnA7sBQoDw4CDwEYvtkkppVKc2GweH77mSQvuM8Z8DUQZY1YYY54HtLetlEpbbOL54WOeTAeMcv95QkSaAMeBYO81SSmlUl6aGuMGhotIFuBVYCyQGXjFq61SSqmUlgp60p66ZeA2xsx3f7wA1PNuc5RSykfSUo9bRCaTwIs47rFupZRKE9LEm5OxzI/1OT3QEtc4t1JKpR1paSMFY8zc2N9FZCaw2mstUkopH0hrDydvVhzImdwNScgX5mJKVKOsJiiLr1ug0qK0NFQiIpeIO8Z9EteblF5nwv5NiWqUhUiuIkQPfsbXzVCpjN+70+6+kLTU4zbGZEqJhiillE9ZKHDfcjReRJZ4kqaUUpZmt3t++FhS63GnB4KA7CKSDbj+6ygzkDcF2qaUUinHQj3upIZKXgT6AnmAzdwI3BeBcd5tllJKpbC0ELiNMZ8Cn4pIb2PM2BRsk1JKpTwLBW5PZpw7RSTr9S8ikk1EXvJek5RSygdsNs+PWxCRSSJySkR2xUp7W0SOicg299E41rk3RWS/iOwTkcdv2VQPbucFY8z561+MMeeAFzy4TimlrEPE8+PWvgEaJpD+sTEmxH385qpWygDtgbLuaz4XkSSfgHoSuO0S65Uid4EBnrRcKaUsIxl73MaYlcBZD2tuDswyxkQaYw4A+4HqSTbVg0IXALNF5BEReQSYCfzuYYOUUsoabiNwi0g3EdkU6+jmYS29RGSHeyglmzstL3AkVp6j3GLmnieBuz+wFOjuPnYCgR42UimlrOE2hkqMMaHGmKqxjlAPapgAFAVCgBPAR3fa1FsGbmOME1iPa6/J6ri2Ldt7pxUqpVSqlLxj3PEYY8KMMQ53TP2SG8Mhx4D8sbLmc6clKqkXcEoAHdzHGWC2u3LdTEEplfZ4eTqgiOQ2xpxwf20JXJ9x8jMwQ0TG4HpvpjiwIamyknoB5y9gFdDUGLPfXbFuWaaUSpOSc/d29/LXdXG9eX4UGArUFZEQXIv2HcT1kiPGmN0iMgfYA0QDPY0xjqTKTypwt8I1RWWZiCwAZnHj7UmllEpbkjFwG2M6JJD8dRL5RwAjPC0/0ZYaY34yxrQHSgHLcL3+nlNEJojIY55WoJRSluDlMe7k5MnDySvGmBnGmGa4Bs23kkLrcSulVIpJxnncXm/q7WQ2xpxzT4N5xFsNUkopn7BQj/tOti5TSqm0JxUEZE9p4FZKKUgVGyR4SgO3UkqB9riVUspyNHArpZTFpILZIp7SwK2UUqA9bqWUshwN3EopZTE6q0QppSxGe9xKKWUxGriVUspiRGeVKKWUtdi0x62UUtaiPW6llLIYnVWilFIWow8nlVLKYnSoRCmlLEZ73EopZTG6yJRSSlmMTR9OKqWUteg87ntPmbpNKFGkUMz3cSOGkC93rgTzVn68JVv++PGu6hvw3kes2bSVxbMmERAQwLnzF2jdrQ9L50y5q3KVlwRmxP7cANfnjFnAOOHKJQAcE4eCw3HXVdifHwiZskJ0FERG4PjpSzhz8q7LvWfow8l7T/p0Afw0aXyK1mm32Zj720I6tGiaovWqOxB+GcfnbwFgq9cScy0S8+dvN87bbOB03nU1ju8mwPEDSNV62B7vgHP6x3dd5j1DH06qK1fD6TlwGBcvXSYq2kHfrp14pPaDcfKcOnOWfm+P5PLVqzgcDob260XViuVYvWEzYyd/S9S1KPLnzc17A/qRISgwXh2dnmzBN3N+4smmjeKd+3rm9/y+bCXXrkXRoE5N+jz/DACfT5nBzwuXEpw1C/fnzEHZEsXo0qGNd/4SVJJsLbtB9DUkdyHM4b8hMjxOQLf3Gonj24/g/BmkYk1sNR4Dux/m6D84f/kGjEm0bHPwL2wPPu6q5/H2SPGKgMG5fB5m13rImAV7u16QLhBsdhy/TIZDf6fAXadi2uO+90REXqPF8z0ByJc7F58MG8S4EYPJmCED585foF2PV6hfqwYS67f6/MXLqFW9Mt07dcDhcBAeGcm58xf4YuosJo8ZSVBger6cPodv5vxAz2c7xqszd64cVKlQhnkLl1C/5gMx6as3bObg0WN8N/FTjDH0eHMYG7ftJH26ABau+JN5kz4nyhFN6y69KVuimPf/clTisgTj+HIYGIOtXsuE8+TIg5SrgePLd8HpwNa0M1KxJmbbn4kWKyUrYcKOIGWqwv0FcYwfCEGZsHcfhuPgX0iFmjj378Ss+NnV0/RP56UbtBAd47733DxUEhUdzZjQKWzavhObzUbY6f84c/YcOe4LjslTvlQJBr3/MVHRDhrUfpDSxYuybNt69h86zFM9X3WVExVFSNnSidbbrWM7eg4cRt0Hq8ek/blxC39u3ELLLr0AuBoezqGjx7ly9Sr1a9UgXboA0hFA3YceSKxYlULMrg1J9pwBpEgZJE8h7N2HuRL8A+DKRRK6yv5kD4i6hjl/Buf8qdgeaoTZudZVx5WLmIN/IfmKYI79i73lCxibHefezXDycPLfnNXorBL1y6JlnDt/gblfjcXfz4/6bTsTeS0qTp5qIeWZNvZDVqzdwJsjx/Bs25ZkzpSRmlUrMWboAI/qKZQ/L6WKF+X3pStj0oxxBfT2zRvHyTtlzt09EFVeEBUZ89E4nXHHWf383R8Es201zkVzblnc9THuWzq0D8fXw5ESIdhbdcO55vcke/D3BAv1uK0zqGMxly9fIThbFvz9/Fi3ZTvHT56Kl+fYyTCyZ8tK22aNaNPkcfb8vZ+QsqXYunMPh44eB+BqeAQHjhxNsq7uz7Rn0uy5Md9rVa/MD78t5MrVcADCTp/hv3PnqVy+DMv/XE9k5DWuXA1n+ZoNyXjH6q6dP43kKeT6nLsgZMsBgPl3N1K2GmTI7DoXmAGy3OdRkebQPqTcA65fCEGZkEKlMEf/dV1/+QJm83Kcm5cjuQsl//1Yjdg8P3xMe9xe0uzRenR/822ade5BuVLFKVIgf7w8G7buYNKsufj52QkKDOT9ga8RnDUrI9/sx6vvjOKau4fet2tnCufPl2hdxQsXpGzxYuz+334AalWvwr+HjtC+Rz8AgoLS8+Fbr1O+dEnqPVSDJ557iezBWSlRpBCZMmbwwt2rO2F2b0RCamHvPRJz5B/474TrxOnjOBd/j73zG64A7HDgmD8FLvx36zL3bELyF8Pe8z3A4PxjFly+gITUwlarCTijITISx9yJ3r05K7DQrBIxtxhf8yFjwv71dRvSnCtXw8kQFEh4RARP936Dd17rQ9mS1nlAKbmKED34GV83Q6Uyfu9Ou+uo65j3ucfB0N78JZ9Gee1x32OGjP6Mfw4eJvLaNVo0bGCpoK2UV+l63Cq1+mhIf183QanUyUJDJb4fZVfxXLx0mT6Dh9Po6Rdo/HQ3tu7aG3Nu0qy5lKrTiHPnL/iwhSol2Fp0xd5/PPZeI+Odk5qN8Ht3GgRldCWkC8TWsR/2niOw9x6JVKqdwq1NA2w2zw8f0x53KjTisy+o/UBVPnv3La5FRRER4ZoydiLsNH9u3EKeXDl93EKVEpxbV8H6Rdhbd497InMwUqwc5vyZmCR5oAGcPoZj+hjXizYvf4Bjx5pkWQPlnqE9bnWnLl2+wqbtu2jTxPW6coC/P5kzuXpVI8dN5PUeXcA6P1/qbhzaB+FX4iXbGnfEuXB2/Bd3AtLf+DP8SrKsfXJP0emA6k4dPXGS4KxZeHPkGPb98y9lSxRnYJ/urN20lVzZs1OqWBFfN1H5kJSqDBfPxXvT0axbhHR8BfsbYyEgPc454275Rqa6ifa4EycizyVxrpuIbBKRTaGhoSnZrFQj2uFgz//206FFE378ejyB6dMzbvK3TPx2Nn266DS4e5p/ALY6T+BcMjfeKSleHk4exvFBbxyfD8LWtDOkS++DRlqY3e75cQsiMklETonIrlhpwSKySET+5/4zmztdROQzEdkvIjtEpPKtyvdFn39YYieMMaHGmKrGmKrdunVLyTalGvfnyE6uHNmpWKYUAI/XrcWev/dz9MRJmj//EvXbdibs9Blade3N6f/O+ri1KkUF54RsOVwPIPuNgczB2Hu8CxmzIJXq4Nyz0ZXv7CnMudOQPY9v22s1yTtU8g3Q8Ka0AcASY0xxYIn7O0AjoLj76AZMuFXhXhkqEZEdiZ0CEt5dQAGQ475gcufMwb+Hj1KkQD7Wbt5GmRLF+OaTUTF56rftzNzQz8iWNYsPW6pSXNhRHO/3jPlq7zcGxxdD4OpluPAftiJlcR76GzJkRrLfD+fiL7OgkpCMQyXGmJUiUuim5OZAXffnKcByoL87fapxvQ25TkSyikhuY8yJxMr31hh3LuBx4NxN6QKs8VKdacZbL/fg9Xc/ICoqivx5cvPem6/4uknKB2xPvoQULg1BGbG/9inOpT9gtqxIMK9z+U/YWnXD3us9QFwPL69eTtkGW91tPHQUkW64esfXhRpjbjW+mytWMD7JjU5sXuBIrHxH3WkpHrjnAxmNMdtuPiEiy71UZ5pRunhR5n75WaLndXuye4Pzu8+TPO8Y0+/Gl0vncU75wMstSuNuY3VAd5C+4wdxxhgjInf89NgrgdsY0yWJc095o06llLor3p/mF3Z9CEREcgPXx7KOAbFXocvnTkuU7yckKqVUamCze37cmZ+Bzu7PnYF5sdI7uWeX1AAuJDW+DTqPWymlAOJsK5gMZc3E9SAyu4gcBYYCo4A5ItIFOAS0dWf/DWgM7AeuAolOmb5OA7cXDRw1huVrNnBftqz8MuULABYsW8W4yd/yz6EjzJn4CeVLlUjw2qnf/cR38xdgjOHJpg3p3Na1H+Ff+/9l6EdjuXo1gry5czJ68BtkzJCBLTt38/ZH4/D39+ejIf0plD8vFy9dpu/Q9/hq9HBsqWB9BeWWORhb6xeRjFkAg3PjMsy6hdja9kSy53blSR8EEVdjdoaPkf1+7G173fieLSfOpXMxa/9wbQpcshI4ojFnT+H88UuIuAoFimNv9iw4onHM+RzOhkH6IGzteuGc+qG+qHNdMg6VGGM6JHLqkQTyGqBnAnkTpYHbi1o2fJSOLZ9gwHujY9KKFy7IZ8MHM3R04g8f//73IN/NX8CciZ/g7+fPC6+/Rd2aD1AwXx7e+uAT3nipK9VDKjD31z/4euZcXu7aicmzfiD0g3c4djKMWfN+Y0CvF5gwdSYvPtNeg3Zq43TgXDADThyCgPTYe7yD459dOOfc2LPU1rADJiI8/rVnTt4I5iLYX/8Ms2cTAGb/Ltf2Zk4ntsfaYavTDOfC2dgeaoRj2mjImgNb9fo4F8zE9nBz10bBGrRvSAWvsnvKOi21oGoh5cmSOVOctKKFClCkQOK72QD8e+gIFUqXJDB9evz87FQLKc+ila79AA8eOUa1iuUBqFm1MgtXrAbAz8+P8MhIwiMi8fezc/jYcU6eOsMDlSp44c7UXbl8wRW0Aa5FYE4fRzIHx8ki5R7A7FibZDFSpCycPRWzE475Z1fM+iTmyH64XqbDAf7pEP8A1+dsOSFLMObgX8l7X1ZnE88PXzfV1w1Q8RUvXJBNO3Zz7sJFwiMiWLFuIydOnQagWKGCLFnt+ge9YPkqTpxyrRDX7em2DBgxmtDpc+jYqhmffDmFl7t28tk9KA9lzY7kLog5uv9GWsGSruB+NizJS6V8DZw7Ew7uUvlhzP+2A+Bc+Qv21i8idZrhXL8I26NP4lz8fbLdQprh/YeTyUaHSlKhooUK8MJTT9Ll1UEEpU9P6WJFsLuHO94b8ArDP53A51NmUv+hGvj7u/4Tli5elNlffALAxm07yXFfMAbDK0NH4udnp3/PF8genM1Xt6QSEpAOe/s+OH+fDpERMcm2Cg/i3LEu6WvtdqRU5QR3fpeHnwCnA7Pd/a7bycM4Qt0rTRQsCZfOgwi2tj1dwza/z4ArF5PppixMh0rU3WrT9HF++Gos3477kMyZMlHIvVlwkYL5mTTmPX74aixNGjxMgTy541xnjGHC1Jn06PwU4ydP57Uez/Nks4ZMmzsvoWqUr9js2Nr3wbljTcwYtSvdhpSpitmVdOCW4hUxJw7GC7hSqTa2EiE4v094uQtb3eautyzrtcT5xyycm5Zje/Cxu72btEHE88PHNHCnUv+dOw/A8bBTLFr5J00b1I2T7nQ6+WLqLNo3bxznup8WLObhGtXImjkTEZGR2MSGTWwxmzGo1MHWsiucPo5ZsyBOuhQpC6dPuJZuTYJUeDDeGLgUK4+tVhMc0z+GqGvxrwmphfl7u2utbv8A14NJ4wT/dHd/Q2mBrsetAPoNG8XGrTs4d+EiD7d+mt7PPUOWzBkZ/ukEzp6/QPf+QylVrAhffzSCsDP/Mfj9Twj98F0A+gwezvkLF/Hz82PIKy/FbKbw6+LlTP9xPgCP1alJq8Y3ekvhERH8uGAxX380AoBn27ai2xtD8Pf3Y7TuNZl6FCiBLaQW5uRh7C8NB8C56DvM/7Yj5R+MP26dKSu2Fl1xTnPPTvJPhxQti3PepDjZbE07g58f9mdd/63Nkf04f/nGfU0AUql2zGvxzjW/Y+/0GkRH4/g+6Vfr7xmp4KGjp8Sk3ulAxoT96+s2qFRGchUherCuS67i8nt32l1HXeeOZR4HQ1uFej6N8trjVkopSBWzRTylgVsppSBVPHT0lAZupZSCVPHQ0VMauJVSCsBCS0No4FZKKZJ3dUBv08CtlFKgQyVKKWU5OqtEKaUsRodKlFLKYvThpFJKWYz2uJVSymL04aRSSlmMBm6llLIYHSpRSimL0cCtlFJWo4FbKaWsRXvcSillMdaJ2xq4lVIK0FklSillOTpUopRSVqOBWymlrEV73EopZTUauJVSylq0x62UUhajs0qUUspadM9JpZSyGg3cSillNRq4lVLKWrTHrZRSFqMPJ5VSymK0x62UUhaTjHFbRA4ClwAHEG2MqSoiwcBsoBBwEGhrjDl3J+Vb5/8NlFLKq+Q2Do/UM8aEGGOqur8PAJYYY4oDS9zf74gGbqWUAtdQiafHnWkOTHF/ngK0uNOCNHArpRTcVuAWkW4isinW0e2m0gywUEQ2xzqXyxhzwv35JJDrTpuqY9xKKQW3NavEGBMKhCaRpZYx5piI5AQWichfN11vRMTcWUO1x62UUi7JOFRijDnm/vMU8CNQHQgTkdyuqiQ3cOpOm6qBWymlgOR6OCkiGUQk0/XPwGPALuBnoLM7W2dg3p22VIdKlFIKknMedy7gR/eiVX7ADGPMAhHZCMwRkS7AIaDtnVYgxtzxMItKISLSzT2mplQM/bm4d+lQiTXc/MRaKdCfi3uWBm6llLIYDdxKKWUxGritQccxVUL05+IepQ8nlVLKYrTHrZRSFqOBWymlLEYDdyonIg1FZJ+I7BeRO14GUqUdIjJJRE6JyC5ft0X5hgbuVExE7MB4oBFQBuggImV82yqVCnwDNPR1I5TvaOBO3aoD+40x/xpjrgGzcK3pq+5hxpiVwFlft0P5jgbu1C0vcCTW96PuNKXUPUwDt1JKWYwG7tTtGJA/1vd87jSl1D1MA3fqthEoLiKFRSQAaI9rTV+l1D1MA3cqZoyJBnoBfwB7gTnGmN2+bZXyNRGZCawFSorIUff6zuoeoq+8K6WUxWiPWymlLEYDt1JKWYwGbqWUshgN3EopZTEauJVSymI0cCuvEBGHiGwTkV0i8p2IBN1FWd+ISBv356+SWmhLROqKSM07qOOgiGS/0zYqlZI0cCtvCTfGhBhjygHXgO6xT4qI350UaozpaozZk0SWusBtB26lrEQDt0oJq4Bi7t7wKhH5GdgjInYR+VBENorIDhF5EUBcxrnXIV8M5LxekIgsF5Gq7s8NRWSLiGwXkSUiUgjXL4hX3L392iKSQ0TmuuvYKCIPua+9T0QWishuEfkKkBT+O1Hqjt1Rr0cpT7l71o2ABe6kykA5Y8wBEekGXDDGVBORdMCfIrIQqASUxLUGeS5gDzDppnJzAF8CddxlBRtjzorIF8BlY8xod74ZwMfGmNUiUgDXW6ilgaHAamPMOyLSBNC3D5VlaOBW3hIoItvcn1cBX+MawthgjDngTn8MqHB9/BrIAhQH6gAzjTEO4LiILE2g/BrAyutlGWMSW5+6AVBGJKZDnVlEMrrraOW+9lcROXdnt6lUytPArbwl3BgTEjvBHTyvxE4Cehtj/rgpX+NkbIcNqGGMiUigLUpZko5xK1/6A+ghIv4AIlJCRDIAK4F27jHw3EC9BK5dB9QRkcLua4Pd6ZeATLHyLQR6X/8iIiHujyuBp9xpjYBsyXVTSnmbBm7lS1/hGr/e4t74diKu/wv8Efif+9xUXCvhxWGMOQ10A34Qke3AbPepX4CW1x9OAn2Aqu6Hn3u4MbtlGK7AvxvXkMlhL92jUslOVwdUSimL0R63UkpZjAZupZSyGA3cSillMRq4lVLKYjRwK6WUxWjgVkopi9HArZRSFvN/kMYQ/xKaFHIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/notebooks/exercises/solution_multilayer_perceptron.ipynb b/notebooks/exercises/solution_multilayer_perceptron.ipynb
index 9f525e2..87ca421 100644
--- a/notebooks/exercises/solution_multilayer_perceptron.ipynb
+++ b/notebooks/exercises/solution_multilayer_perceptron.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilayer Perceptron"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -7,8 +14,7 @@
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "\n"
+    "from tensorflow import keras"
    ]
   },
   {
diff --git a/notebooks/exercises/solution_random_forest.ipynb b/notebooks/exercises/solution_random_forest.ipynb
index 13d63e8..7dc533f 100644
--- a/notebooks/exercises/solution_random_forest.ipynb
+++ b/notebooks/exercises/solution_random_forest.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random Forest"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/notebooks/exercises/solution_recurrent_neural_network.ipynb b/notebooks/exercises/solution_recurrent_neural_network.ipynb
index b033d1c..dc18959 100644
--- a/notebooks/exercises/solution_recurrent_neural_network.ipynb
+++ b/notebooks/exercises/solution_recurrent_neural_network.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recurrent Neural Network"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 55,
@@ -74,7 +81,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x720 with 25 Axes>"
       ]
diff --git a/notebooks/exercises/solution_single_layer_perceptron.ipynb b/notebooks/exercises/solution_single_layer_perceptron.ipynb
index bf0155d..2d9844e 100644
--- a/notebooks/exercises/solution_single_layer_perceptron.ipynb
+++ b/notebooks/exercises/solution_single_layer_perceptron.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Single Layer Perceptron"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -63,7 +70,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
-- 
GitLab


From 459a4556dbe36ccafc0846ece1d3b4f609b81611 Mon Sep 17 00:00:00 2001
From: Kai-Philipp Nosper <kai-philipp.nosper@stud.h-da.de>
Date: Mon, 24 Jan 2022 22:13:51 +0100
Subject: [PATCH 3/5] Rename Single-Layer Perceptron and Multi-Layer Perceptron

---
 .../MLP.png                                   | Bin
 .../Multi-Layer_Perceptron.md}                |  28 +++++++++---------
 .../NAND.jpg                                  | Bin
 .../XOR.jpg                                   | Bin
 .../XORSolution.jpg                           | Bin
 .../XOR_NANDOR.png                            | Bin
 .../0.png                                     | Bin
 .../1.png                                     | Bin
 .../Single_Layer_Perceptron.md                |   8 ++---
 book/src/Exercises.md                         |   4 +--
 book/src/References.md                        |  12 ++++----
 book/src/SUMMARY.md                           |   4 +--
 ...ron.ipynb => multi-layer_perceptron.ipynb} |   2 +-
 ...on.ipynb => single-layer_perceptron.ipynb} |   2 +-
 ... => exercise_multi-layer_perceptron.ipynb} |   4 +--
 ...=> exercise_single-layer_perceptron.ipynb} |   8 ++---
 ... => solution_multi-layer_perceptron.ipynb} |   2 +-
 ...=> solution_single-layer_perceptron.ipynb} |   2 +-
 18 files changed, 38 insertions(+), 38 deletions(-)
 rename book/src/AI-Models/{MultilayerPerceptron => Multi-Layer_Perceptron}/MLP.png (100%)
 rename book/src/AI-Models/{MultilayerPerceptron/MultilayerPerceptron_documentation.md => Multi-Layer_Perceptron/Multi-Layer_Perceptron.md} (71%)
 rename book/src/AI-Models/{MultilayerPerceptron => Multi-Layer_Perceptron}/NAND.jpg (100%)
 rename book/src/AI-Models/{MultilayerPerceptron => Multi-Layer_Perceptron}/XOR.jpg (100%)
 rename book/src/AI-Models/{MultilayerPerceptron => Multi-Layer_Perceptron}/XORSolution.jpg (100%)
 rename book/src/AI-Models/{MultilayerPerceptron => Multi-Layer_Perceptron}/XOR_NANDOR.png (100%)
 rename book/src/AI-Models/{Single_Layer_Perceptron => Single-Layer_Perceptron}/0.png (100%)
 rename book/src/AI-Models/{Single_Layer_Perceptron => Single-Layer_Perceptron}/1.png (100%)
 rename book/src/AI-Models/{Single_Layer_Perceptron => Single-Layer_Perceptron}/Single_Layer_Perceptron.md (92%)
 rename notebooks/examples/{multi_layer_perceptron.ipynb => multi-layer_perceptron.ipynb} (99%)
 rename notebooks/examples/{single_layer_perceptron.ipynb => single-layer_perceptron.ipynb} (99%)
 rename notebooks/exercises/{exercise_multilayer_perceptron.ipynb => exercise_multi-layer_perceptron.ipynb} (93%)
 rename notebooks/exercises/{exercise_single_layer_perceptron.ipynb => exercise_single-layer_perceptron.ipynb} (92%)
 rename notebooks/exercises/{solution_multilayer_perceptron.ipynb => solution_multi-layer_perceptron.ipynb} (99%)
 rename notebooks/exercises/{solution_single_layer_perceptron.ipynb => solution_single-layer_perceptron.ipynb} (99%)

diff --git a/book/src/AI-Models/MultilayerPerceptron/MLP.png b/book/src/AI-Models/Multi-Layer_Perceptron/MLP.png
similarity index 100%
rename from book/src/AI-Models/MultilayerPerceptron/MLP.png
rename to book/src/AI-Models/Multi-Layer_Perceptron/MLP.png
diff --git a/book/src/AI-Models/MultilayerPerceptron/MultilayerPerceptron_documentation.md b/book/src/AI-Models/Multi-Layer_Perceptron/Multi-Layer_Perceptron.md
similarity index 71%
rename from book/src/AI-Models/MultilayerPerceptron/MultilayerPerceptron_documentation.md
rename to book/src/AI-Models/Multi-Layer_Perceptron/Multi-Layer_Perceptron.md
index 924bd27..7b59042 100644
--- a/book/src/AI-Models/MultilayerPerceptron/MultilayerPerceptron_documentation.md
+++ b/book/src/AI-Models/Multi-Layer_Perceptron/Multi-Layer_Perceptron.md
@@ -1,18 +1,18 @@
-# Multilayer Perceptron
+# Multi-Layer Perceptron
 
-The Multilayer Perceptron is a class of feedforward artificial neural network. Frank Rosenblatt who invented the
-Single Layer Perceptron also invented the Multilayer Perceptron in 1958. While the Single Layer Perceptron is only 
-capable of solving classification problems where a linear separation is necessary, the Multilayer Perceptron is
-capable of solving problems like XOR. MLPs are as suitable as Single Layer Perceptrons for classification problems where inputs are assigned a class or label.
+The multi-layer perceptron is a class of feedforward artificial neural network. Frank Rosenblatt who invented the
+single-layer perceptron also invented the multi-layer perceptron in 1958. While the single-layer perceptron is only 
+capable of solving classification problems where a linear separation is necessary, the multi-layer perceptron is
+capable of solving problems like XOR. MLPs are as suitable as single-layer perceptrons for classification problems where inputs are assigned a class or label.
 They are also suitable for regression prediction problems where a real-valued quantity is predicted given a set of inputs.
 
 ## Theory
-On the Single Layer Perceptron we already saw the algorithm performing on the OR gate. Here we are going to take a look
-how the Single Layer Perceptron is performing on the NAND gate first and then on the XOR gate. Afterwards there will be
-a explanation why it didn't work on the XOR gate and why it works with our Multilayer Perceptron.
+On the single-layer perceptron we already saw the algorithm performing on the OR gate. Here we are going to take a look
+how the single layer perceptron is performing on the NAND gate first and then on the XOR gate. Afterwards there will be
+a explanation why it didn't work on the XOR gate and why it works with our multi-layer perceptron.
 
 First of let's give an explanation as to why we are showing a NAND and a XOR gate. Simply because we know that an OR and AND
-gate is solvable for the Single Layer Perceptron. When an AND gate is solvable that implicates for our NAND gate that
+gate is solvable for the single-layer perceptron. When an AND gate is solvable that implicates for our NAND gate that
 it is solvable as well.
 
 | x1 | x2 | y |
@@ -39,7 +39,7 @@ Layer Perceptron. Let's take a look at the XOR gate on a linear basis down below
 
 A classification with a linear basis is not possible because we can't separate the green and blue points with a single line. 
 That's why we need a different approach. 
-To understand why an additional Layer (Multilayer Perceptron) can solve our problem, we are going to take a closer look what XOR is exactly.
+To understand why an additional Layer (multi-layer perceptron) can solve our problem, we are going to take a closer look what XOR is exactly.
 
 >\\(XOR(A,B) = A * \stackrel{-}B + B * \stackrel{-}A \\)
 >
@@ -69,21 +69,21 @@ that means if we would have an additional layer we could solve the XOR problem.
 
 ![XOR Solution](XORSolution.jpg)
 
-The Multilayer Perceptron can solve the XOR problem because of this hidden layers. That is 
+The multi-layer perceptron can solve the XOR problem because of this hidden layers. That is 
 the reason why the algorithm is capable of solving more than basic linear relation classification.
 
 ## Structure
-The structure in a fully connected Multilayer Perceptron looks like this: 
+The structure in a fully connected multi-layer perceptron looks like this: 
 
 ![MLP](MLP.png)
 
-We need three different things in order to create a Multilayer Perceptron. First there are the input neurons, 
+We need three different things in order to create a multi-layer perceptron. First there are the input neurons, 
 followed by one or more hidden layers. Lastly the output neurons.
 Fully connected means that every single unit of one layer is connected to every other unit from the next layer.
 Every connection between our neurons has its own weight which is randomly set in the beginning.
 Afterwards weights are updated by the backpropagation algorithm which is used for other neural networks as well.
 
 ## References
-{{#include ../../References.md:Multi_Layer_Perceptron}}
+{{#include ../../References.md:Multi-Layer_Perceptron}}
 
 *Written by Lukas Pfahlert*
\ No newline at end of file
diff --git a/book/src/AI-Models/MultilayerPerceptron/NAND.jpg b/book/src/AI-Models/Multi-Layer_Perceptron/NAND.jpg
similarity index 100%
rename from book/src/AI-Models/MultilayerPerceptron/NAND.jpg
rename to book/src/AI-Models/Multi-Layer_Perceptron/NAND.jpg
diff --git a/book/src/AI-Models/MultilayerPerceptron/XOR.jpg b/book/src/AI-Models/Multi-Layer_Perceptron/XOR.jpg
similarity index 100%
rename from book/src/AI-Models/MultilayerPerceptron/XOR.jpg
rename to book/src/AI-Models/Multi-Layer_Perceptron/XOR.jpg
diff --git a/book/src/AI-Models/MultilayerPerceptron/XORSolution.jpg b/book/src/AI-Models/Multi-Layer_Perceptron/XORSolution.jpg
similarity index 100%
rename from book/src/AI-Models/MultilayerPerceptron/XORSolution.jpg
rename to book/src/AI-Models/Multi-Layer_Perceptron/XORSolution.jpg
diff --git a/book/src/AI-Models/MultilayerPerceptron/XOR_NANDOR.png b/book/src/AI-Models/Multi-Layer_Perceptron/XOR_NANDOR.png
similarity index 100%
rename from book/src/AI-Models/MultilayerPerceptron/XOR_NANDOR.png
rename to book/src/AI-Models/Multi-Layer_Perceptron/XOR_NANDOR.png
diff --git a/book/src/AI-Models/Single_Layer_Perceptron/0.png b/book/src/AI-Models/Single-Layer_Perceptron/0.png
similarity index 100%
rename from book/src/AI-Models/Single_Layer_Perceptron/0.png
rename to book/src/AI-Models/Single-Layer_Perceptron/0.png
diff --git a/book/src/AI-Models/Single_Layer_Perceptron/1.png b/book/src/AI-Models/Single-Layer_Perceptron/1.png
similarity index 100%
rename from book/src/AI-Models/Single_Layer_Perceptron/1.png
rename to book/src/AI-Models/Single-Layer_Perceptron/1.png
diff --git a/book/src/AI-Models/Single_Layer_Perceptron/Single_Layer_Perceptron.md b/book/src/AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md
similarity index 92%
rename from book/src/AI-Models/Single_Layer_Perceptron/Single_Layer_Perceptron.md
rename to book/src/AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md
index 0265f58..f8f74d7 100644
--- a/book/src/AI-Models/Single_Layer_Perceptron/Single_Layer_Perceptron.md
+++ b/book/src/AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md
@@ -1,5 +1,5 @@
-# Single Layer Perceptron
-The Single Layer Perceptron is a simple neural network model that is used to classify data. It is a linear combination of the input data and a weight vector. The output of the model is the output of the perceptron.
+# Single-Layer Perceptron
+The single-layer perceptron is a simple neural network model that is used to classify data. It is a linear combination of the input data and a weight vector. The output of the model is the output of the perceptron.
 The Perceptron algorithm was developed by Frank Rosenblatt in the 1960s. The Perceptron Model still represents the most common neural network model and is used in fields such as machine learning, pattern recognition and computer vision.
 Later, the Perceptron was used in the development of the Multilayer Perceptron.
 
@@ -17,7 +17,7 @@ We have the following dataset which represents an OR gate:
 
 As you can see it is possible to split the dataset into two parts. The first part is the output with the label `1` and the second part is the output with the label `0`. Since you can draw a straight line between them to seperate the two outcomes we know that the dataset is valid.
 
-The Single Layer perceptron gets three values as input.
+The single-layer perceptron gets three values as input.
 
 The first value is usually the BIAS or Base value to normalize the dataset. The second value is x1 and the third value is x2.\
 In the first step the input values are multiplied with the weight vector (value next to the arrows).\
@@ -66,6 +66,6 @@ After only a few epochs the accuracy has already reached 1.0 which means the mod
 Even though the accuracy is already at 100% in epoch 6 the model is still improving the bias, x1 and x2 values with every epoch.
 
 ## References
-{{#include ../../References.md:Single_Layer_Perceptron}}
+{{#include ../../References.md:Single-Layer_Perceptron}}
 
 *Written by Kai-Philipp Nosper*
\ No newline at end of file
diff --git a/book/src/Exercises.md b/book/src/Exercises.md
index b12328a..b112eb5 100644
--- a/book/src/Exercises.md
+++ b/book/src/Exercises.md
@@ -15,8 +15,8 @@ These are the exercises you will find on your Pi in the folder exercises.
 | Random Forest| Adult | Predict if person makes more than 50k a year|Niklas Speckels|
 | K-Means | auto-mpg | cluster mpg of cars based on technical data|Kevin de Riese-Meyer|
 | K-Nearest Neighbors| Titanic| Prediction of passenger survival|Tobias Stein|
-| Single Layer Perceptron |  | Predict an AND-Gate |Kai-Philipp Nosper|
-| Multilayer Perceptron | Fashion-mnist | Classify different clothes|Lukas Pfahlert|
+| Single-Layer Perceptron |  | Predict an AND-Gate |Kai-Philipp Nosper|
+| Multi-Layer Perceptron | Fashion-mnist | Classify different clothes|Lukas Pfahlert|
 | Deep Learning | emnist-letters | classify handwritten letters |Kevin de Riese-Meyer|
 | Convolutional Neural Network | mnist-Digits | Classify handwritten digits |Felix Matthias Krumm|
 | Recurrent Neural Network| mnist fashion| Clothing article classification|Niklas Speckels|
diff --git a/book/src/References.md b/book/src/References.md
index 7a5f649..e444466 100644
--- a/book/src/References.md
+++ b/book/src/References.md
@@ -63,10 +63,10 @@ ANCHOR: AUTOENCODER
 [7] [Definition: deconvolutional networks (last accessed on 08.01.2022)](https://www.techtarget.com/searchenterpriseai/definition/deconvolutional-networks-deconvolutional-neural-networks)
 ANCHOR_END: AUTOENCODER
 
-### Single Layer Perceptron
-ANCHOR: Single_Layer_Perceptron
+### Single-Layer Perceptron
+ANCHOR: Single-Layer_Perceptron
 [1] [Using neural nets to recognize handwritten digits (last accessed on 15.01.2022)](http://neuralnetworksanddeeplearning.com/chap1.html)
-ANCHOR_END: Single_Layer_Perceptron
+ANCHOR_END: Single-Layer_Perceptron
 
 ### Logistic Regression
 ANCHOR: Logistic_Regression
@@ -99,10 +99,10 @@ ANCHOR: Intelligence_and_Learning
 [5] [Wikipedia: Turing-Test (last accessed on 06.01.2022)](https://de.wikipedia.org/wiki/Turing-Test)
 ANCHOR_END: Intelligence_and_Learning
 
-### Multi Layer Perceptron
-ANCHOR: Multi_Layer_Perceptron
+### Multi-Layer Perceptron
+ANCHOR: Multi-Layer_Perceptron
 [1] [How Neural Networks Solve the XOR Problem (last accessed on 17.01.2022)](https://towardsdatascience.com/how-neural-networks-solve-the-xor-problem-59763136bdd7)
-ANCHOR_END: Multi_Layer_Perceptron
+ANCHOR_END: Multi-Layer_Perceptron
 
 ### Recurrent Neural Network
 ANCHOR: Recurrent_neural_network
diff --git a/book/src/SUMMARY.md b/book/src/SUMMARY.md
index c0a10fe..b227f0f 100644
--- a/book/src/SUMMARY.md
+++ b/book/src/SUMMARY.md
@@ -23,8 +23,8 @@
         - [K-Means](./AI-Models/K-Means/K-Means.md)
         - [K-Nearest Neighbors](./AI-Models/K-Nearest-Neighbors/K-Nearest-Neighbors.md)
     - [Neural Networks](./AI-Models/Neural-Networks.md)
-        - [Single Layer Perceptron](./AI-Models/Single_Layer_Perceptron/Single_Layer_Perceptron.md)
-        - [Multi-Layer-Perceptron](./AI-Models/MultilayerPerceptron/MultilayerPerceptron_documentation.md)
+        - [Single-Layer Perceptron](./AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md)
+        - [Multi-Layer Perceptron](./AI-Models/Multi-Layer_Perceptron/Multi-Layer_Perceptron.md)
         - [Deep Neural Network](./AI-Models/Deep_Neural_Network/Deep_Neural_Network.md)
         - [Convolutional Neural Networks](./AI-Models/Convolutional-Neural-Networks/Convolutional-Neural-Networks.md)
         - [Auto-Encoder](./AI-Models/Autoencoder/Autoencoder.md)
diff --git a/notebooks/examples/multi_layer_perceptron.ipynb b/notebooks/examples/multi-layer_perceptron.ipynb
similarity index 99%
rename from notebooks/examples/multi_layer_perceptron.ipynb
rename to notebooks/examples/multi-layer_perceptron.ipynb
index 653f01e..8c1e3a3 100644
--- a/notebooks/examples/multi_layer_perceptron.ipynb
+++ b/notebooks/examples/multi-layer_perceptron.ipynb
@@ -5,7 +5,7 @@
    "id": "86975b4a",
    "metadata": {},
    "source": [
-    "# Multi Layer Perceptron"
+    "# Multi-Layer Perceptron"
    ]
   },
   {
diff --git a/notebooks/examples/single_layer_perceptron.ipynb b/notebooks/examples/single-layer_perceptron.ipynb
similarity index 99%
rename from notebooks/examples/single_layer_perceptron.ipynb
rename to notebooks/examples/single-layer_perceptron.ipynb
index 6822e11..200fb71 100644
--- a/notebooks/examples/single_layer_perceptron.ipynb
+++ b/notebooks/examples/single-layer_perceptron.ipynb
@@ -5,7 +5,7 @@
    "id": "7a974be2",
    "metadata": {},
    "source": [
-    "# Singler Layer Perceptron"
+    "# Single-Layer Perceptron"
    ]
   },
   {
diff --git a/notebooks/exercises/exercise_multilayer_perceptron.ipynb b/notebooks/exercises/exercise_multi-layer_perceptron.ipynb
similarity index 93%
rename from notebooks/exercises/exercise_multilayer_perceptron.ipynb
rename to notebooks/exercises/exercise_multi-layer_perceptron.ipynb
index fcc3481..7ad5e99 100644
--- a/notebooks/exercises/exercise_multilayer_perceptron.ipynb
+++ b/notebooks/exercises/exercise_multi-layer_perceptron.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multilayer Perceptron"
+    "# Multi-Layer Perceptron"
    ]
   },
   {
@@ -63,7 +63,7 @@
    "source": [
     "# Task 3\n",
     "\n",
-    "Build a Multilayer Perceptron with Sequential and compile it afterwards using the following :\n",
+    "Build a multi-layer perceptron with Sequential and compile it afterwards using the following :\n",
     "\n",
     "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
     "             optimizer=\"sgd\",\n",
diff --git a/notebooks/exercises/exercise_single_layer_perceptron.ipynb b/notebooks/exercises/exercise_single-layer_perceptron.ipynb
similarity index 92%
rename from notebooks/exercises/exercise_single_layer_perceptron.ipynb
rename to notebooks/exercises/exercise_single-layer_perceptron.ipynb
index 58628c8..345f7eb 100644
--- a/notebooks/exercises/exercise_single_layer_perceptron.ipynb
+++ b/notebooks/exercises/exercise_single-layer_perceptron.ipynb
@@ -4,16 +4,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Single Layer Perceptron\n",
+    "# Single-Layer Perceptron\n",
     "\n",
-    "In this execise your task is to implement an AND-Gate as Single Layer Perceptron.\n",
+    "In this execise your task is to implement an AND-Gate as single-layer Perceptron.\n",
     "You can start at your own or follow the following tasks.\n",
     "\n",
-    "In the single layer perceptron example you saw how to implement a single layer perceptron as OR-Gate. The AND-Gate is almost the same procedure but with a different dataset.\n",
+    "In the single-layer perceptron example you saw how to implement a single-layer perceptron as OR-Gate. The AND-Gate is almost the same procedure but with a different dataset.\n",
     "\n",
     "## Task\n",
     "\n",
-    "Your task is to copy and modify the single layer perceptron example to implement an AND-Gate. For this follow the steps in the exercise notebook for the single layer perceptron. If you get stucked first try to remember the example notebook and after that take a look into it if necessary. After you finished your exercise compare it with the proposed solution."
+    "Your task is to copy and modify the single-layer perceptron example to implement an AND-Gate. For this follow the steps in the exercise notebook for the single-layer perceptron. If you get stucked first try to remember the example notebook and after that take a look into it if necessary. After you finished your exercise compare it with the proposed solution."
    ]
   },
   {
diff --git a/notebooks/exercises/solution_multilayer_perceptron.ipynb b/notebooks/exercises/solution_multi-layer_perceptron.ipynb
similarity index 99%
rename from notebooks/exercises/solution_multilayer_perceptron.ipynb
rename to notebooks/exercises/solution_multi-layer_perceptron.ipynb
index 87ca421..c0f6b82 100644
--- a/notebooks/exercises/solution_multilayer_perceptron.ipynb
+++ b/notebooks/exercises/solution_multi-layer_perceptron.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multilayer Perceptron"
+    "# Multi-Layer Perceptron"
    ]
   },
   {
diff --git a/notebooks/exercises/solution_single_layer_perceptron.ipynb b/notebooks/exercises/solution_single-layer_perceptron.ipynb
similarity index 99%
rename from notebooks/exercises/solution_single_layer_perceptron.ipynb
rename to notebooks/exercises/solution_single-layer_perceptron.ipynb
index 2d9844e..4447000 100644
--- a/notebooks/exercises/solution_single_layer_perceptron.ipynb
+++ b/notebooks/exercises/solution_single-layer_perceptron.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Single Layer Perceptron"
+    "# Single-Layer Perceptron"
    ]
   },
   {
-- 
GitLab


From 43aad17ccbc1946b1b004dfd805f270a2947c170 Mon Sep 17 00:00:00 2001
From: Kai-Philipp Nosper <kai-philipp.nosper@stud.h-da.de>
Date: Mon, 24 Jan 2022 22:18:44 +0100
Subject: [PATCH 4/5] Fix RNN references

---
 book/src/References.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/book/src/References.md b/book/src/References.md
index e444466..062dcfb 100644
--- a/book/src/References.md
+++ b/book/src/References.md
@@ -106,8 +106,8 @@ ANCHOR_END: Multi-Layer_Perceptron
 
 ### Recurrent Neural Network
 ANCHOR: Recurrent_neural_network
-[1] [Recurrent Neural Networks (last accessed on 18.01.2022)] (https://www.ibm.com/cloud/learn/recurrent-neural-networks)\
-[2] [Recurrent Neural Networks cheatsheet (last accessed on 18.01.2022)] (https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks)
+[1] [Recurrent Neural Networks (last accessed on 18.01.2022)](https://www.ibm.com/cloud/learn/recurrent-neural-networks)\
+[2] [Recurrent Neural Networks cheatsheet (last accessed on 18.01.2022)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks)
 ANCHOR_END: Recurrent_neural_network
 
 ANCHOR_END: ALL
-- 
GitLab


From c061ff2303eb5d3e76e93716c79a205ddca56e20 Mon Sep 17 00:00:00 2001
From: Kai-Philipp Nosper <kai-philipp.nosper@stud.h-da.de>
Date: Mon, 24 Jan 2022 22:48:11 +0100
Subject: [PATCH 5/5] Rename slp mdbook file

---
 .../{Single_Layer_Perceptron.md => Single-Layer_Perceptron.md}  | 0
 book/src/SUMMARY.md                                             | 2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename book/src/AI-Models/Single-Layer_Perceptron/{Single_Layer_Perceptron.md => Single-Layer_Perceptron.md} (100%)

diff --git a/book/src/AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md b/book/src/AI-Models/Single-Layer_Perceptron/Single-Layer_Perceptron.md
similarity index 100%
rename from book/src/AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md
rename to book/src/AI-Models/Single-Layer_Perceptron/Single-Layer_Perceptron.md
diff --git a/book/src/SUMMARY.md b/book/src/SUMMARY.md
index b227f0f..12c85c6 100644
--- a/book/src/SUMMARY.md
+++ b/book/src/SUMMARY.md
@@ -23,7 +23,7 @@
         - [K-Means](./AI-Models/K-Means/K-Means.md)
         - [K-Nearest Neighbors](./AI-Models/K-Nearest-Neighbors/K-Nearest-Neighbors.md)
     - [Neural Networks](./AI-Models/Neural-Networks.md)
-        - [Single-Layer Perceptron](./AI-Models/Single-Layer_Perceptron/Single_Layer_Perceptron.md)
+        - [Single-Layer Perceptron](./AI-Models/Single-Layer_Perceptron/Single-Layer_Perceptron.md)
         - [Multi-Layer Perceptron](./AI-Models/Multi-Layer_Perceptron/Multi-Layer_Perceptron.md)
         - [Deep Neural Network](./AI-Models/Deep_Neural_Network/Deep_Neural_Network.md)
         - [Convolutional Neural Networks](./AI-Models/Convolutional-Neural-Networks/Convolutional-Neural-Networks.md)
-- 
GitLab