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notebooks/lab-8/sl-tpl-ml-part-1.ipynb

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"A big part of Netflix's business strategy is being able to predict what kind of movies a user will enjoy watching, based on how they rate previous movies they've seen. This is such a big deal to the company that they offered [one million dollars as a prize](https://en.wikipedia.org/wiki/Netflix_Prize) to anyone who could beat their algorithm.\n",
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"Let's put this problem in formal terms. Suppose you have a matrix, where each row represents a user and each column represents a movie, like this one:\n",
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"![A matrix with rows representing users, columns representing movies, and the entries representing a user's movie for a specific rating. This matrix contains ratings by Parth, Michael, Joy, and Unicornelius for the movies Harry Potter and the Goblet of Fire, Unicorn Killer, Inside Out, and Frozen 2](movie_matrix.png)\n",
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"![A matrix with rows representing users, columns representing movies, and the entries representing a user's movie for a specific rating. This matrix contains ratings by Parth, Michael, Joy, and Unicornelius for the movies Harry Potter and the Goblet of Fire, Unicorn Killer, Inside Out, and Frozen 2](https://raw.githubusercontent.com/stanfordpython/python-labs/master/notebooks/lab-8/movie_matrix.png)\n",
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"One version of the Netflix problem (which is the one that the prize money was for) is to *complete* the matrix. You'll notice that some of the boxes above are marked with `?`. That indicates that we don't have a rating from that user for that movie. Netflix offered a million dollars to any algorithm that could predict the values of those question markes better than their algorithm.\n",
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"\n",

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