diff --git a/10_nlp.ipynb b/10_nlp.ipynb
index b1f81c8..656ed19 100644
--- a/10_nlp.ipynb
+++ b/10_nlp.ipynb
@@ -103,7 +103,7 @@
     "\n",
     "- **Tokenization**:: convert the text into a list of words (or characters, or substrings, depending on the granularity of your model)\n",
     "- **Numericalization**:: make a list of all of the unique words which appear (the vocab), and convert each word into a number, by looking up its index in the vocab\n",
-    "- **Language model data loader** creation:: fastai provides an `LMDataLoader` class which automatically handles creating a dependent variable which is offset from the independent variable buy one token. It also handles some important details, such as how to shuffle the training data in such a way that the dependent and independent variables maintain their structure as required\n",
+    "- **Language model data loader** creation:: fastai provides an `LMDataLoader` class which automatically handles creating a dependent variable which is offset from the independent variable by one token. It also handles some important details, such as how to shuffle the training data in such a way that the dependent and independent variables maintain their structure as required\n",
     "- **Language model** creation:: we need a special kind of model which does something we haven't seen before: handles input lists which could be arbitrarily big or small. There are a number of ways to do this; in this chapter we will be using a *recurrent neural network*. We will get to the details of this in the <<chapter_nlp_dive>>, but for now, you can think of it as just another deep neural network.\n",
     "\n",
     "Let's take a look at how each step works in detail."
@@ -347,7 +347,7 @@
     "\n",
     "Here is a brief summary of what each does:\n",
     "\n",
-    "- `fix_html`:: replace special HTML characters by a readable version (IMDb reviwes have quite a few of them for instance) ;\n",
+    "- `fix_html`:: replace special HTML characters by a readable version (IMDb reviews have quite a few of them for instance) ;\n",
     "- `replace_rep`:: replace any character repeated three times or more by a special token for repetition (xxrep), the number of times it's repeated, then the character ;\n",
     "- `replace_wrep`:: replace any word repeated three times or more by a special token for word repetition (xxwrep), the number of times it's repeated, then the word ;\n",
     "- `spec_add_spaces`:: add spaces around / and # ;\n",
@@ -1276,7 +1276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we have seen at the beginning of this chapter to train a state-of-the-art text classifier using transfer learning will take two steps: first we need to fine-tune our langauge model pretrained on Wikipedia to the corpus of IMDb reviews, then we can use that model to train a classifier.\n",
+    "As we have seen at the beginning of this chapter to train a state-of-the-art text classifier using transfer learning will take two steps: first we need to fine-tune our language model pretrained on Wikipedia to the corpus of IMDb reviews, then we can use that model to train a classifier.\n",
     "\n",
     "As usual, let's start with assembling our data."
    ]
@@ -1515,7 +1515,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can them finetune the model after unfreezing:"
+    "We can then finetune the model after unfreezing:"
    ]
   },
   {
@@ -2251,6 +2251,18 @@
    "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,