diff --git a/01_intro.ipynb b/01_intro.ipynb
index 695dcdb..e8eebe5 100644
--- a/01_intro.ipynb
+++ b/01_intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2188,11 +2188,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "This model is using the IMDb dataset from the paper [Learning Word Vectors for Sentiment Analysis]((https://ai.stanford.edu/~amaas/data/sentiment/)). It works well with movie reviews of many thousands of words. But let's test it out on a very short one, to see it does its thing:"
    ]
@@ -2200,11 +2196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2302,29 +2294,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "> jargon: Tabular: Data that is in the form of a table, such as from a spreadsheet, database, or CSV file. A tabular model is a model which tries to predict one column of a table based on information in other columns of a table."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from fastai2.tabular.all import *\n",
     "path = untar_data(URLs.ADULT_SAMPLE)\n",
     "\n",
-    "dls = TabularDataLoaders.from_csv(path/'adult.csv', path, y_names=\"salary\",\n",
+    "dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names=\"salary\",\n",
     "    cat_names = ['workclass', 'education', 'marital-status', 'occupation',\n",
     "                 'relationship', 'race'],\n",
     "    cont_names = ['age', 'fnlwgt', 'education-num'],\n",
@@ -2335,11 +2319,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "As you see, we had to tell fastai which columns are *categorical* (that is, they contain values that are one of a discrete set of choices, such as `occupation`), versus *continuous* (that is, they contain a number that represents a quantity, such as `age`).\n",
     "\n",
@@ -2349,11 +2329,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2407,11 +2383,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "This model is using the *adult* dataset, from the paper [Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid](https://archive.ics.uci.edu/ml/datasets/adult), which contains some data regarding individuals (like their education, marital status, race, sex, etc.) and whether or not they have an annual income greater than \\$50k. The model is over 80\\% accurate, and took around 30 seconds to train.\n",
     "\n",
@@ -2421,11 +2393,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2550,22 +2518,13 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "This model is predicting movie ratings on a scale of 0.5 to 5.0 to within around 0.6 average error. Since we're predicting a continuous number, rather than a category, we have to tell fastai what range our target has, using the `y_range` parameter.\n",
     "\n",
     "Although we're not actually using a pretrained model (for the same reason that we didn't for the tabular model), this example shows that fastai lets us use `fine_tune` even in this case (we'll learn how and why this works later in <<chapter_pet_breeds>>). Sometimes it's best to experiment with `fine_tune` versus `fit_one_cycle` to see which works best for your dataset.\n",
     "\n",
     "We can use the same `show_results` call we saw earlier to view a few examples of user and movie IDs, actual ratings, and predictions:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "learn.show_results()"
    ]
   },
   {
@@ -2969,17 +2928,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.4"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "source": [],
-    "metadata": {
-     "collapsed": false
-    }
-   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/clean/01_intro.ipynb b/clean/01_intro.ipynb
index c9ad32e..b3f94c3 100644
--- a/clean/01_intro.ipynb
+++ b/clean/01_intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1135,14 +1135,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from fastai2.tabular.all import *\n",
     "path = untar_data(URLs.ADULT_SAMPLE)\n",
     "\n",
-    "dls = TabularDataLoaders.from_csv(path/'adult.csv', path, y_names=\"salary\",\n",
+    "dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names=\"salary\",\n",
     "    cat_names = ['workclass', 'education', 'marital-status', 'occupation',\n",
     "                 'relationship', 'race'],\n",
     "    cont_names = ['age', 'fnlwgt', 'education-num'],\n",