Merge pull request #47 from StevenBorg/master

Correct two spelling errors in 04_mnist_basics

04_mnist_basics.ipynb

@@ -3641,7 +3641,7 @@
     "\n",
     "Another good reason for using mini-batches rather than calculating the gradient on individual data items is that, in practice, we nearly always do our training on an accelerator such as a GPU. These accelerators only perform well if they have lots of work to do at a time. So it is helpful if we can give them lots of data items to work on at a time. Using mini-batches is one of the best ways to do this. However, if you give them too much data to work on at once, they run out of memory--making GPUs happy is also tricky!.\n",
     "\n",
-    "As we've seen, in the discussion of data augmentation, we get better generalisation if we can very things during training. A simple and effective thing we can vary during training is what data items we put in each mini batch. Rather than simply enumerating our data set in order for every epoch, instead what we normally do in practice is to randomly shuffle it on every epoch, before we create mini batches. PyTorch and fastai provide a class that will do the shuffling and mini batch collation for you, called `DataLoader`.\n",
+    "As we've seen, in the discussion of data augmentation, we get better generalisation if we can vary things during training. A simple and effective thing we can vary during training is what data items we put in each mini batch. Rather than simply enumerating our data set in order for every epoch, instead what we normally do in practice is to randomly shuffle it on every epoch, before we create mini batches. PyTorch and fastai provide a class that will do the shuffling and mini batch collation for you, called `DataLoader`.\n",
     "\n",
     "A `DataLoader` can take any Python collection, and turn it into an iterator over many batches, like so:"
    ]
@@ -4836,7 +4836,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The basic idea is that by using more linear layers, we can have our model do more computation, and therefore model more complex functions. But there's no point just putting one linear layout directly after another one, because when we multiply things together and then at them up multiple times, that can be replaced by multiplying different things together and adding them up just once! That is to say, a series of any number of linear layers in a row can be replaced with a single linear layer with a different set of parameters.\n",
+    "The basic idea is that by using more linear layers, we can have our model do more computation, and therefore model more complex functions. But there's no point just putting one linear layout directly after another one, because when we multiply things together and then add them up multiple times, that can be replaced by multiplying different things together and adding them up just once! That is to say, a series of any number of linear layers in a row can be replaced with a single linear layer with a different set of parameters.\n",
     "\n",
     "But if we put a non-linear function between them, such as max, then this is no longer true. Now, each linear layer is actually somewhat decoupled from the other ones, and can do its own useful work. The max function is particularly interesting, because it operates as a simple \"if\" statement. For any arbitrarily wiggly function, we can approximate it as a bunch of lines joined together; to make it more close to the wiggly function, we just have to use shorter lines."
    ]

README.md

@@ -12,4 +12,4 @@ If you see someone hosting a copy of these materials somewhere else, please let
 
 This is an early draft. If you get stuck running notebooks, please search the [fastai-v2 forum](https://forums.fast.ai/c/fastai-users/fastai-v2) for answers, and ask for help there if needed. Please don't use GitHub issues for problems running the notebooks.
 
-If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger.
+If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and very brief description of what you're fixing. It's becoming increasingly difficult for reviewers to know which corrections have already been made. Thank you.)