Follow-up

clean/03_ethics.ipynb

@@ -304,10 +304,25 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/12_nlp_dive.ipynb

@@ -1553,7 +1553,7 @@
    "metadata": {},
    "source": [
     "1. If the dataset for your project is so big and complicated that working with it takes a significant amount of time, what should you do?\n",
-    "1. Why do we concatenating the documents in our dataset before creating a language model?\n",
+    "1. Why do we concatenate the documents in our dataset before creating a language model?\n",
     "1. To use a standard fully connected network to predict the fourth word given the previous three words, what two tweaks do we need to make?\n",
     "1. How can we share a weight matrix across multiple layers in PyTorch?\n",
     "1. Write a module which predicts the third word given the previous two words of a sentence, without peeking.\n",
@@ -1626,6 +1626,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,