Update notebook, fix typos

redi/ws_19/lectures/00_orientation_session/00_orientation_session.ipynb

@@ -48,9 +48,11 @@
     "## Iman \n",
     "\n",
     "## Nima\n",
+    "- Did a PhD in Deep Learning for Computer Vision, Uni Freiburg\n",
+    "- Mainly pursuing research and teaching in Deep Learning for Astronomy!\n",
     "\n",
     "## Florian \n",
-    "- Studied Statistics at LMU Munich\n",
+    "- Studied Statistics at LMU Munich, currently getting a PhD\n",
     "- Worked as a data scientist in industry and startups\n",
     "\n",
     "## Muzahid\n",
@@ -113,23 +115,25 @@
     "# Example\n",
     "## We are going to start with the famous IRIS dataset!\n",
     "The iris dataset is a classic and very easy multi-class classification dataset.\n",
-    "<img src=\"figures/irisClasses.png\"/>\n",
-    "\n",
-    "|                  |   |\n",
-    "|------------------|---|\n",
-    "|classes           |3  |\n",
-    "|Sample per Classes|50 |\n",
-    "|Total Samples     |150|\n",
-    "|Dimentionality    |4  |\n",
-    "|Features          |real, Positive|\n",
-    "\n",
-    "\n",
-    "\n"
+    "<img src=\"figures/irisClasses.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Attribute        |  x |\n",
+    "|------------------|----|\n",
+    "|Classes           |3   |\n",
+    "|Sample per Class  |50  |\n",
+    "|Total Samples     |150 |\n",
+    "|N. of features    |4   |\n",
+    "|Type of features  |double|\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-01-21T15:54:19.260519Z",
@@ -149,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-01-21T16:28:28.583716Z",
@@ -168,9 +172,7 @@
       ]
      },
      "execution_count": 3,
-     "metadata": {
-      "tags": []
-     },
+     "metadata": {},
      "output_type": "execute_result"
     }
    ],
@@ -185,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-01-21T16:28:42.804546Z",
@@ -201,7 +203,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<built-in method values of Bunch object at 0x7fe4ff77dd00>\n",
       "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
      ]
     }
@@ -226,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-01-21T16:57:39.831688Z",
@@ -276,10 +277,10 @@
       " [5.4 3.4 1.5 0.4]\n",
       " [5.2 4.1 1.5 0.1]\n",
       " [5.5 4.2 1.4 0.2]\n",
-      " [4.9 3.1 1.5 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
       " [5.  3.2 1.2 0.2]\n",
       " [5.5 3.5 1.3 0.2]\n",
-      " [4.9 3.6 1.4 0.1]\n",
+      " [4.9 3.1 1.5 0.1]\n",
       " [4.4 3.  1.3 0.2]\n",
       " [5.1 3.4 1.5 0.2]\n",
       " [5.  3.5 1.3 0.3]\n",
@@ -393,18 +394,6 @@
       " [6.2 3.4 5.4 2.3]\n",
       " [5.9 3.  5.1 1.8]]\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(150, 4)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -441,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-01-21T16:58:06.563439Z",
@@ -636,7 +625,7 @@
    },
    "source": [
     "##  Were you able to follow and are you curious what happens next?\n",
-    "### Join us for the course and we will work on real-life Machine Learning Projects together"
+    "### Join us for the course and we will work on real-life Machine Learning Projects together!\n"
    ]
   },
   {
@@ -652,15 +641,21 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 0,
+   "cell_type": "markdown",
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "ql0Ghn7W8RRr"
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "### Preparing for the course:\n",
+    "* Introduction to Python: https://www.kaggle.com/learn/python\n",
+    "* Introduction to Python: https://www.datacamp.com/courses/intro-to-python-for-data-science\n",
+    "* Intro to Numpy: http://cs231n.github.io/python-numpy-tutorial/\n",
+    "* Intro to Numpy: https://hackernoon.com/introduction-to-numpy-1-an-absolute-beginners-guide-to-machine-learning-and-data-science-5d87f13f0d51\n",
+    "\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {