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293244 (1) [Avatar] Offline

Thanks for the book on Machine Learning with TensorFlow. The clarification section on "When to use a metric other than L2 norm in real world" - Chapter 1, was not exactly very clear. Could you elaborate some more on that part ?

Nishant Shukla (52) [Avatar] Offline
Sure! I rephrased it a little to make it easier to understand. You'll see it in the latest book update.
Let’s say you’re working for a new search-engine start-up trying to compete with Google.
Your boss assigns you the task of using machine learning to personalize the search results for each user.
A good goal might be that users shouldn’t see 5 or more incorrect search-results per month.
A year’s worth of user-data is a 12-dimension vector (where each month of the year is a dimension), indicating number of incorrect results shown per month.
You are trying to satisfy the condition that the L-infinity norm of this vector must below 5.
Suppose instead that your boss changes his/her mind and requires that less than 5 erroneous search-results are allowed for the entire year.
In this case, you are trying to achieve a L1 norm below 5.
Actually, your boss changes his or her mind again.
Now, the number of months with erroneous search-results should be less than 5.
In that case, you are trying to achieve an L0 norm below 5.