There's quite a bit of misinformation in this comment.
- Tensorflow has very little use for the mathematical concept of a "Tensor", apart from the fact that it is a multidimensional array as a way of organizing data.
- Again, most of what is covered in an Information theory class is coding theory, which is not directly applicable to ML. There are a few superficial connections, however, nothing enough to justify a whole class.
- A class on Harmonic analysis, again, though a beautiful subject, does not have any significant overlap with ML, apart from a few superficial similarities to do with convolution.
- Most ML Ph.d.s don't take these classes, and go on to have very successful careers.
This comment is very typical of a kind of snobbery in ML observers that goes along the lines of "you need to understand all these deep and hard concepts before you start to touch ML". Actually, you don;t. ML is, right now, still quite a young field as far as its branching off from statistics goes. We are still building the groundwork of this skyscraper.
We welcome everyone with any background, and hey, even those with none.
he misses many points, including the meaning of the phrase "long-term greedy". It is not about "everyone else in the market was short-term greedy and, as a result, we took all their money. Since traders like money, this was inspiring.", but a philosophy of enlightened self-interest, which I suspect would resonate with the class and professor more. Yet he subtly uses this as an anecdote about how MBAs are dumb and the classes "herd mentality". What aarogance!
if you've read the article, you'll the discussion at hand was concerning stereoscopic 3d, not the fancy light field tech which may be around the corner, nor not.
- Tensorflow has very little use for the mathematical concept of a "Tensor", apart from the fact that it is a multidimensional array as a way of organizing data.
- Again, most of what is covered in an Information theory class is coding theory, which is not directly applicable to ML. There are a few superficial connections, however, nothing enough to justify a whole class.
- A class on Harmonic analysis, again, though a beautiful subject, does not have any significant overlap with ML, apart from a few superficial similarities to do with convolution.
- Most ML Ph.d.s don't take these classes, and go on to have very successful careers.
This comment is very typical of a kind of snobbery in ML observers that goes along the lines of "you need to understand all these deep and hard concepts before you start to touch ML". Actually, you don;t. ML is, right now, still quite a young field as far as its branching off from statistics goes. We are still building the groundwork of this skyscraper.
We welcome everyone with any background, and hey, even those with none.