As someone who works in machine learning, I have mixed feelings about this article. While encouraging people to start learning about ML by demystifying it is a great thing, this article comes off as slightly cocky and dangerous. Programmers who believe they understand ML while only having a simplistic view of it risk not only to create less-than-optimal algorithms, and might instead create downright dangerous models:
http://static.squarespace.com/static/5150aec6e4b0e340ec52710...In the context of fraud detection (one of the main areas I work in these days), a model that is right for the wrong reasons might lead to catastrophic losses when the underlying assumption that made the results valid suddenly ceases to be true.
Aside from the fact the techniques he mentioned are some of the simplest in machine learning (and are hardly those that would immediately come to mind when I think "machine learning"), the top comment on the article is spot on:
> "The academic papers are introducing new algorithms and proving properties about them, you’re applying the result. You’re standing on giants’ shoulders and thinking it’s easy to see as far as they do."
While understanding how the algorithm works is of course important (and I do agree that they are often more readable when translated to code), understanding why (and when) they work is equally important. Does each K-Means iteration always reach a stable configuration? When can you expect it to converge fast? How do you choose the number of clusters, and how does this affect convergence speed? Does the way you initialize your centroids have a significant effect on the outcome? If yes, which initializations tend to work better in which situations?
These are all questions I might ask in an interview, but more importantly, being able to answer these is often the difference between blindly applying a technique and applying it intelligently. Even for "simple" algorithms such as K-Means, implementing them is often only the tip of the iceberg.
In the context of fraud detection (one of the main areas I work in these days), a model that is right for the wrong reasons might lead to catastrophic losses when the underlying assumption that made the results valid suddenly ceases to be true.
Aside from the fact the techniques he mentioned are some of the simplest in machine learning (and are hardly those that would immediately come to mind when I think "machine learning"), the top comment on the article is spot on:
> "The academic papers are introducing new algorithms and proving properties about them, you’re applying the result. You’re standing on giants’ shoulders and thinking it’s easy to see as far as they do."
While understanding how the algorithm works is of course important (and I do agree that they are often more readable when translated to code), understanding why (and when) they work is equally important. Does each K-Means iteration always reach a stable configuration? When can you expect it to converge fast? How do you choose the number of clusters, and how does this affect convergence speed? Does the way you initialize your centroids have a significant effect on the outcome? If yes, which initializations tend to work better in which situations?
These are all questions I might ask in an interview, but more importantly, being able to answer these is often the difference between blindly applying a technique and applying it intelligently. Even for "simple" algorithms such as K-Means, implementing them is often only the tip of the iceberg.