Better markdown based on PR comments

DeepSpeech.ipynb

@@ -714,7 +714,7 @@
     "    accuracy = tf.reduce_mean(distance)\n",
     "```\n",
     "\n",
-    "Finally, the `total_loss` and `avg_loss`, the `distance` and `accuracy`, the `decoded` batch and the original `batch_y` are returned to the caller\n",
+    "Finally, the calculated total and average losses, the Levenshtein distance and the recognition accuracy are returned, alongside the decoded batch and the original batch_y (which contains the verified transcriptions).\n",
     "```python\n",
     "    # Return results to the caller\n",
     "    return total_loss, avg_loss, distance, accuracy, decoded, batch_y\n",
@@ -894,15 +894,16 @@
     " * the CTC decodings ```decoded```,\n",
     " * the (total) loss against the outcome (Y) ```total_loss```, \n",
     " * the loss averaged over the whole batch ```avg_loss```,\n",
-    " * the optimization gradient (computed on base of the averaged loss),\n",
-    " * the distances between the decodings and the originals ```distance``` and\n",
+    " * the optimization gradient (computed based on the averaged loss),\n",
+    " * the Levenshtein distances between the decodings and their transcriptions ```distance```,\n",
     " * the accuracy of the outcome averaged over the whole batch ```accuracy``` \n",
     " \n",
     "and retain the original ```labels``` (Y).\n",
     " \n",
-    "```decoded```, ```labels```, the optimization gradient, ```distance```, ```accuracy```, ```total_loss``` and ```avg_loss``` are collected into the respective arrays ```tower_decodings, tower_labels, tower_gradients, tower_distances, tower_accuracies, tower_total_losses, tower_avg_losses``` (dimension 0 being the tower).\n",
+    "```decoded```, ```labels```, the optimization gradient, ```distance```, ```accuracy```, ```total_loss``` and ```avg_loss``` are collected into the corresponding arrays ```tower_decodings, tower_labels, tower_gradients, tower_distances, tower_accuracies, tower_total_losses, tower_avg_losses``` (dimension 0 being the tower).\n",
     "\n",
-    "Finally this new method `get_tower_results()` will return those tower arrays either directly or in case of ```tower_accuracies``` the averaged accuracy value and in case of ```tower_avg_losses``` the averaged loss value accross all towers."
+    "Finally this new method `get_tower_results()` will return those tower arrays.\n",
+    "In case of ```tower_accuracies``` and ```tower_avg_losses```, it will return the averaged values instead."
    ]
   },
   {
@@ -1210,7 +1211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another routine will help collecting partial results for the WER reports. The ```results_tuple``` is composed of an array of the original labels, an array of the corrsponding decodings, an array of the corrsponding distances and an array of the corresponding losses. ```returns``` is built up in a similar way, containing just the unprocessed results of one ```session.run``` call (effectively of one batch). Before splicing them into their corresponding ```results_tuple``` lists, labels and decodings are converted to text. In the case of decodings, for now we just pick the first available path."
+    "Another routine will help collecting partial results for the WER reports. The ```results_tuple``` is composed of an array of the original labels, an array of the corresponding decodings, an array of the corrsponding distances and an array of the corresponding losses. ```returns``` is built up in a similar way, containing just the unprocessed results of one ```session.run``` call (effectively of one batch). Labels and decodings are converted to text before splicing them into their corresponding results_tuple lists. In the case of decodings, for now we just pick the first available path."
    ]
   },
   {