Update electricity.ipynb

docs/examples/electricity.ipynb

@@ -444,7 +444,7 @@
     "id": "4a82175c-56c8-454a-925f-9cabfbedd079"
    },
    "source": [
-    "The first thing you'll notice about this approach is that it's **incredibly slow to train**. The second problem is that the forecasts are **terrible**:"
+    "The problem is that the forecasts are **terrible**:"
    ]
   },
   {
@@ -495,10 +495,9 @@
    "source": [
     "### Attempt 2\n",
     "\n",
-    "Let's see if we can improve on this. We'll leave the model unchanged but make two changes:\n",
+    "Let's see if we can improve on this. We'll leave the model unchanged, but train it differently. We'll use the `n_step` argument to train our model on one-week ahead forecasts, instead of one-hour ahead. This improves the efficiency of training by encouraging the model to 'care about' longer range forecasts vs. over-focusing on the easier problem of forecasting the next hour.\n",
     "\n",
-    "- Use the `n_step` argument to train our model on one-week ahead forecasts, instead of one step (i.e. hour) ahead. This improves the efficiency of training by encouraging the model to 'care about' longer range forecasts vs. over-focusing on the easier problem of forecasting the next hour.\n",
-    "- Split our single series into multiple groups. This is helpful to speed up training, since pytorch has a non-trivial overhead for separate tensors: i.e., it scales well with an increasing batch-size (fewer, but bigger, tensors), but poorly with an increasing time-series length (smaller, but more, tensors)."
+    "Another thing we'll address is the fact that the simple (and lousy) model above was surprisingly slow to train. This is because Pytorch has a non-trivial overhead for separate tensors: i.e., it scales well with an increasing batch-size (fewer, but bigger, tensors), but poorly with an increasing time-series length (smaller, but more, tensors). So to speed things up, we'll split our single series into multiple groups."
    ]
   },
   {