improve task 2 text

exercises/01_penguin_classification.ipynb

@@ -57,7 +57,7 @@
     "\n",
     "The penguin data reading and processing can be encapsulated in a PyTorch dataset class.\n",
     "\n",
-    "- Why is this helpful?\n",
+    "- Why is a class representation helpful?\n",
     "\n",
     "All PyTorch dataset objects are subclasses of the ``torch.utils.data.Dataset`` class. To make a custom dataset, create a class which inherits from the ``Dataset`` class, implement some methods (the Python magic (or dunder) methods ``__len__`` and ``__getitem__``) and supply some data.\n",
     "\n",

worked-solutions/01_penguin_classification_solutions.ipynb

@@ -110,15 +110,14 @@
     "\n",
     "The penguin data reading and processing can be encapsulated in a PyTorch dataset class.\n",
     "\n",
-    "- Why is this class representation helpful?\n",
+    "- Why is a class representation helpful?\n",
     "  - Modularity - Separation of concerns makes the cde easier to understand, maintain and test.\n",
     "  - Maintainability - Changes are localised, therefore we only need to change a single file to update. \n",
     "  - Abstraction - Users do not need to know how the data is read or processed, they only need to know how to interact with the class. \n",
     "\n",
-    "\n",
     "All PyTorch dataset objects are subclasses of the ``torch.utils.data.Dataset`` class. To make a custom dataset, create a class which inherits from the ``Dataset`` class, implement some methods (the Python magic (or dunder) methods ``__len__`` and ``__getitem__``) and supply some data.\n",
     "\n",
-    "Spoiler alert: we've done this for you already in ``src/ml_workshop/_penguins.py``.\n",
+    "Spoiler alert: we've done this for you already below (see ``src/ml_workshop/_penguins.py`` for a more sophisticated implementation)\n",
     "\n",
     "- Open the file ``src/ml_workshop/_penguins.py``.\n",
     "- Let's examine, and discuss, each of the methods together.\n",