Improve task 3 text and remove x_tfms and y_tfms comments

Author: ma595

exercises/01_penguin_classification.ipynb

@@ -74,9 +74,7 @@
     "- Review and discuss the class arguments.\n",
     "  - ``input_keys``— ...\n",
     "  - ``target_keys``— ...\n",
-    "  - ``train``— ...\n",
-    "  - ``x_tfms``— ...\n",
-    "  - ``y_tfms``— ..."
+    "  - ``train``— ..."
    ]
   },
   {
@@ -230,7 +228,7 @@
    "source": [
     "- Can we give these items to a neural network, or do they need to be transformed first?\n",
     "  - Short answer: no, we can't just pass tuples of numbers or strings to a neural network.\n",
-    "    - We must represent these data as ``torch.Tensor``s. This is the fundamental data abstraction used by PyTorch. See [pytorch tensors documentation](https://pytorch.org/tutorials/beginner/introyt/tensors_deeper_tutorial.html).\n",
+    "    - We must represent these data as ``torch.Tensor``s. This is the fundamental data abstraction used by PyTorch; they are the PyTorch equivalent to Numpy arrays, while also providing support for GPU acceleration. See [pytorch tensors documentation](https://pytorch.org/tutorials/beginner/introyt/tensors_deeper_tutorial.html).\n",
     "    - The targets are tuples of strings i.e. ('Gentoo', )\n",
     "      - One idea is to represent as ordinal values i.e.  [1] or [2] or [3]. But this implies that the class encoded by value 1 is closer to 2 than 1 is to 3. This is not desirable for categorical data. One-hot encoding avoids this by representing each species independently.\\\n",
     "      \"A\" — [1, 0, 0]\\\n",