diff --git a/02_lab/lab1_regression_faces.ipynb b/02_lab/lab1_regression_faces.ipynb
index 22ccf3b..dc34349 100644
--- a/02_lab/lab1_regression_faces.ipynb
+++ b/02_lab/lab1_regression_faces.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -24,7 +26,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.datasets import fetch_olivetti_faces\n",
@@ -34,7 +38,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# @this code showcases matplotlib subplots. The syntax is: plt.subplot(height, width, index_starting_from_1)\n",
@@ -56,12 +62,14 @@
     "\n",
     "Let's solve the face reconstruction problem: given left halves of facex __(X)__, our algorithm shall predict the right half __(y)__. Our first step is to slice the photos into X and y using slices.\n",
     "\n",
+    "__Slices in numpy:__\n",
     "* In regular python, slice looks roughly like this: `a[2:5]` _(select elements from 2 to 5)_\n",
     "* Numpy allows you to slice N-dimensional arrays along each dimension: [image_index, height, width]\n",
     "  * `data[:10]` - Select first 10 images\n",
     "  * `data[:, :10]` - For all images, select a horizontal stripe 10 pixels high at the top of the image\n",
     "  * `data[10:20, :, -25:-15]` - Take images [10, 11, ..., 19], for each image select a _vetrical stripe_ of width 10 pixels, 15 pixels away from the _right_ side.\n",
     "\n",
+    "__Your task:__\n",
     "\n",
     "Let's use slices to select all __left image halves as X__ and all __right halves as y__."
    ]
@@ -69,7 +77,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# select left half of each face as X, right half as Y\n",
@@ -80,7 +90,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# If you did everything right, you're gonna see left half-image and right half-image drawn separately in natural order\n",
@@ -95,7 +107,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def glue(left_half,right_half):\n",
@@ -119,7 +133,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -133,7 +149,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
@@ -151,7 +169,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.metrics import mean_squared_error\n",
@@ -163,7 +183,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Train predictions\n",
@@ -177,7 +199,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Test predictions\n",
@@ -247,7 +271,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "<YOUR CODE: fit the model on training set>"