initial draft of part 1

Author: piskvorky

0 - Intro & Setup.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ecffde91d7d9cdabed666a66cf5c1ef5e07bba4c4e14f0b9d4f6742ec575cc8e"
+  "signature": "sha256:d47ab3bdbb8947837ed647f17839e988544b737b9660a40130ff49b8b17c1d91"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -39,7 +39,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Check all dependencies are installed correctly (see the [README](https://github.com/piskvorky/topic_modeling_tutorial/)) (highlight each cell with your mouse and press SHIFT+ENTER):"
+      "Check all dependencies are installed correctly (see the [README](https://github.com/piskvorky/topic_modeling_tutorial/)) (highlight each cell with your mouse and press `SHIFT`+`ENTER`):"
      ]
     },
     {
@@ -461,6 +461,23 @@
       "\n",
       "A common pattern that we'll be using is **combining the efficiency of in-memory arrays** (numpy, scipy.sparse) with the **scalability of data streaming**. Instead of processing one document at a time (slow), or all documents at once (non-scalable), we'll be reading **a chunk of documents** into RAM (= as many documents as RAM allows), processing this chunk, then throwing it away and streaming a new chunk into RAM."
      ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Notebooks"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "At any point, you can save the notebook (any notebook) to disk by pressing `CTRL`+`s` or `CMD`+`s`. This will **save all your changes**, including cell outputs.\n",
+      "\n",
+      "To discard your notebook changes, simply checkout the file again from git (or extract it again from the repository ZIP archive). This will reset the notebook to its original state, **losing all changes**."
+     ]
     }
    ],
    "metadata": {}