Update tutorial formatting

Tutorials/CNTK_208_Speech_Connectionist_Temporal_Classification.ipynb

@@ -13,7 +13,7 @@
     "* [MLF file with labels](https://github.com/Microsoft/CNTK/blob/master/Tests/EndToEndTests/Speech/Data/glob_0000.mlf)\n",
     "* [States list file](https://github.com/Microsoft/CNTK/blob/master/Tests/EndToEndTests/Speech/Data/state_ctc.list)\n",
     "\n",
-    "The example state list file contains the CTC blank label \"s_blank\" as the last entry, i.e. at index 132.\n",
+    "The example state list file contains the CTC blank label `s_blank` as the last entry, i.e. at index 132.\n",
     "\n",
     "## Feature Input Definition\n"
    ]
@@ -30,9 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -80,9 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "feature_mean = np.fromfile(os.path.join(\"GlobalStats\", \"mean.363\"), dtype=float, count=feature_dimension)\n",
@@ -104,15 +100,13 @@
    },
    "source": [
     "## Define Training Parameters, Criteria and Error\n",
-    "CTC criteria is implemented by combination of the **labels_to_graph** and **forward_backward** functions."
+    "CTC criteria is implemented by combination of the `labels_to_graph` and `forward_backward` functions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mbsize = 1024\n",
@@ -137,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -184,15 +176,13 @@
    "source": [
     "# Test the Model \n",
     "\n",
-    "For simplicity, we will use a portion of the train set for testing here"
+    "For simplicity, we will use a portion of the train set for testing here."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -207,32 +197,14 @@
     "\n",
     "print(round(trainer.test_minibatch(test_data), 2))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [cntk-py35]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "Python [cntk-py35]"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {