Docs: note dataset location in workspace

Author: Michael Virgo

project-dog-classification/README.md

@@ -24,10 +24,13 @@ Along with exploring state-of-the-art CNN models for classification and localiza
 		git clone https://github.com/udacity/deep-learning-v2-pytorch.git
 		cd deep-learning-v2-pytorch/project-dog-classification
 	```
-3. Download the [dog dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip).  Unzip the folder and place it in the repo, at location `path/to/dog-project/dogImages`.  The `dogImages/` folder should contain 133 folders, each corresponding to a different dog breed.
-4. Download the [human dataset](http://vis-www.cs.umass.edu/lfw/lfw.tgz).  Unzip the folder and place it in the repo, at location `path/to/dog-project/lfw`.  If you are using a Windows machine, you are encouraged to use [7zip](http://www.7-zip.org/) to extract the folder. 
-5. Make sure you have already installed the necessary Python packages according to the README in the program repository.
-6. Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.
+	
+__NOTE:__ if you are using the Udacity workspace, you *DO NOT* need to re-download the datasets in steps 2 and 3 - they can be found in the `/data` folder as noted within the workspace Jupyter notebook.
+
+2. Download the [dog dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip).  Unzip the folder and place it in the repo, at location `path/to/dog-project/dogImages`.  The `dogImages/` folder should contain 133 folders, each corresponding to a different dog breed.
+3. Download the [human dataset](http://vis-www.cs.umass.edu/lfw/lfw.tgz).  Unzip the folder and place it in the repo, at location `path/to/dog-project/lfw`.  If you are using a Windows machine, you are encouraged to use [7zip](http://www.7-zip.org/) to extract the folder. 
+4. Make sure you have already installed the necessary Python packages according to the README in the program repository.
+5. Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.
 
 	```
 		jupyter notebook dog_app.ipynb