img rename optmization formatting

Author: cassiebreviu

_static/img/quickstart/optimization_loops.PNG renamed to _static/img/quickstart/optimizationloops.png

@@ -11,8 +11,8 @@
 #
 
 ###############################################################
-# .. figure:: /_static/img/quickstart/typesofdata.png
-#    :alt: typesofdata
+# .. figure:: /_static/img/quickstart/typesdata.png
+#    :alt: typesdata
 # 
 
 ############################################################

_static/img/quickstart/typesofdata.PNG renamed to _static/img/quickstart/typesdata.png

@@ -40,7 +40,7 @@
 #
 
 ############################################################
-# .. figure:: /_static/img/quickstart/optimization_loops.png
+# .. figure:: /_static/img/quickstart/optimizationloops.png
 #    :alt:
 #
 

beginner_source/quickstart/data_quickstart_tutorial.py

@@ -61,7 +61,7 @@
 # For the feature transforms we have an array of transforms to process our image data for training. The first transform in the array is `transforms.ToTensor()` this is from class [torchvision.transforms.ToTensor](https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.ToTensor). We need to take our images and turn them into a tensor. (To learn more about Tensors check out [this]() resource.) The ToTensor() transformation is doing more than converting our image into a tensor. Its also normalizing our data for us by scaling the images to be between 0 and 1.
 #
 #
-# .. note: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling.
+# .. note:: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling.
 #
 #
 # Check out the other `TorchVision Transforms <https://pytorch.org/docs/stable/torchvision/transforms.html>`_