Merge pull request #226 from gruggiero/gruggiero-patch-1

Update 01_intro.ipynb

01_intro.ipynb

@@ -1541,7 +1541,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Pet dataset contains 7,390 pictures of dogs and cats, consisting of 37 different breeds. Each image is labeled using its filename: for instance the file *great\\_pyrenees\\_173.jpg* is the 173rd example of an image of a Great Pyrenees breed dog in the dataset. The filenames start with an uppercase letter if the image is a cat, and a lowercase letter otherwise. We have to tell fastai how to get labels from the filenames, which we do by calling `from_name_func` (which means that filenames can be extracted using a function applied to the filename), and passing `x[0].isupper()`, which evaluates to `True` if the first letter is uppercase (i.e., it's a cat).\n",
+    "The Pet dataset contains 7,390 pictures of dogs and cats, consisting of 37 different breeds. Each image is labeled using its filename: for instance the file *great\\_pyrenees\\_173.jpg* is the 173rd example of an image of a Great Pyrenees breed dog in the dataset. The filenames start with an uppercase letter if the image is a cat, and a lowercase letter otherwise. We have to tell fastai how to get labels from the filenames, which we do by calling `from_name_func` (which means that labels can be extracted using a function applied to the filename), and passing `x[0].isupper()`, which evaluates to `True` if the first letter is uppercase (i.e., it's a cat).\n",
     "\n",
     "The most important parameter to mention here is `valid_pct=0.2`. This tells fastai to hold out 20% of the data and *not use it for training the model at all*. This 20% of the data is called the *validation set*; the remaining 80% is called the *training set*. The validation set is used to measure the accuracy of the model. By default, the 20% that is held out is selected randomly. The parameter `seed=42` sets the *random seed* to the same value every time we run this code, which means we get the same validation set every time we run it—this way, if we change our model and retrain it, we know that any differences are due to the changes to the model, not due to having a different random validation set.\n",
     "\n",