some more clarifications

00_Introduction.ipynb

@@ -49,7 +49,7 @@
     "\n",
     "  * `mkdir -p ~/mlpractical/repos-3rd`\n",
     "\n",
-    "And now, let us set an MLP_WDIR environmental variable (MLP Working DIRectory) that denotes an absolute path of working dir pointing to `~/mlpractial`, **add the below line** to your `~/.bashrc` file (if it does not exists, create one!):\n",
+    "And now, let us set an MLP_WDIR environmental variable (MLP Working DIRectory) that will keep an absolute path of working dir pointing to `~/mlpractial`, **add the below line** to your `~/.bashrc` file (if it does not exists, create one!):\n",
     "\n",
     "```\n",
     "export MLP_WDIR=~/mlpractical\n",
@@ -66,11 +66,13 @@
     "  * `git clone https://github.com/pypa/virtualenv`\n",
     "  * Enter the cloned repository and type ```./virtualenv.py --python /usr/bin/python2.7 --no-site-packages $MLP_WDIR/venv```\n",
     "  * Activate the environment by typing `source ~/mlpractical/venv/bin/activate` (to leave the virtual environment one may type `decativate`)\n",
-    "  * Environments need to be activated every time ones start the new session so add this command to your `~/.bashrc` script, by typing the below command (note, MLP_WDIR export needs to preceed this command and `.` is actually important):\n",
+    "  * Environments need to be activated every time ones start the new session so we will now create a handy alias to it in `~/.bashrc` script, by typing the below command (note, MLP_WDIR export needs to preceed this command):\n",
     "  \n",
-    "  ```. $MLP_WDIR/venv/bin/activate```  \n",
+    "  ```alias activate_mlp=\"source $MLP_WDIR/venv/bin/activate\"```\n",
+    "  \n",
+    "Then every time you open new session and want to activate the right virtual environment, simply type `activate_mlp` instead `source ~/mlpractical/venv/bin/activate`. Note, you need to re-soure the .bashrc in order alias to be visible in the current session.\n",
     "\n",
-    "### Install packages\n",
+    "### Installing remaining packages\n",
     "\n",
     "Then, before you follow next, install/upgrade the following packages:\n",
     "\n",
@@ -348,7 +350,9 @@
    "source": [
     "### Exercise 3\n",
     "\n",
-    "Write your own data provider `MetOfficeDataProvider` that wraps the weather data for south Scotland (could be obtained from: http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). The file was also downloaded and stored in `data` directory for your convenience. The provider should return a tuple `(x,t)` of the estimates over an arbitrary time windows (i.e. last N-1 days) for `x` and the N-th day as the one which model should be able to predict, `t`. For now, skip missing data-points (denoted by -99.9) and simply use the next correct value. Make sure the provider works for arbitrary `batch_size` settings, including the case where single mini-batch is equal to all datapoints in the dataset. Test the dataset in the cell below."
+    "Write your own data provider `MetOfficeDataProvider` that wraps the weather data for south Scotland (could be obtained from: http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). The file was also downloaded and stored in `data` directory for your convenience. The provider should return a tuple `(x,t)` of the estimates over an arbitrary time windows (i.e. last N-1 days) for `x` and the N-th day as the one which model should be able to predict, `t`. For now, skip missing data-points (denoted by -99.9) and simply use the next correct value. Make sure the provider works for arbitrary `batch_size` settings, including the case where single mini-batch is equal to all datapoints in the dataset. Test the dataset in the cell below.\n",
+    "\n",
+    "Tip: To follow with this exercise, copy MNISTDataProvider in dataset.py, rename it to `MetOfficeDataProvider` and reimplement necesarry parts (including the arguments you pass to the constructor)."
    ]
   },
   {