Superfluous prose in clean version of 01_intro.ipynb

clean/01_intro.ipynb

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-    "### Limitations Inherent To Machine Learning\n",
-    "\n",
-    "From this picture we can now see some fundamental things about training a deep learning model:\n",
-    "\n",
-    "- A model cannot be created without data.\n",
-    "- A model can only learn to operate on the patterns seen in the input data used to train it.\n",
-    "- This learning approach only creates *predictions*, not recommended *actions*.\n",
-    "- It's not enough to just have examples of input data; we need *labels* for that data too (e.g., pictures of dogs and cats aren't enough to train a model; we need a label for each one, saying which ones are dogs, and which are cats).\n",
-    "\n",
-    "Generally speaking, we've seen that most organizations that say they don't have enough data, actually mean they don't have enough *labeled* data. If any organization is interested in doing something in practice with a model, then presumably they have some inputs they plan to run their model against. And presumably they've been doing that some other way for a while (e.g., manually, or with some heuristic program), so they have data from those processes! For instance, a radiology practice will almost certainly have an archive of medical scans (since they need to be able to check how their patients are progressing over time), but those scans may not have structured labels containing a list of diagnoses or interventions (since radiologists generally create free-text natural language reports, not structured data). We'll be discussing labeling approaches a lot in this book, because it's such an important issue in practice.\n",
-    "\n",
-    "Since these kinds of machine learning models can only make *predictions* (i.e., attempt to replicate labels), this can result in a significant gap between organizational goals and model capabilities. For instance, in this book you'll learn how to create a *recommendation system* that can predict what products a user might purchase. This is often used in e-commerce, such as to customize products shown on a home page by showing the highest-ranked items. But such a model is generally created by looking at a user and their buying history (*inputs*) and what they went on to buy or look at (*labels*), which means that the model is likely to tell you about products the user already has or already knows about, rather than new products that they are most likely to be interested in hearing about. That's very different to what, say, an expert at your local bookseller might do, where they ask questions to figure out your taste, and then tell you about authors or series that you've never heard of before."
-   ]
+    "### Limitations Inherent To Machine Learning\n"
+    ]
   },
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