npp

5 ms·

1.
by npp 11y ago
In the same vein -- John Nash once said that Minsky was the smartest person he ever met. ...He also said that he thought McCarthy was an idiot. :)
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by npp 11y ago
This article reminds me of a lot of what Dalton Caldwell spoke about in his talk about why not to start a music startup at a Startup School in the last several years. This business seems to have been run and have proceeded horribly, for the
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by npp 13y ago
Your blog post makes an important mistake in that it recommends actually inverting the matrix X^T X, which one never actually does in this case. In general, A^{-1} b is sort of mathematical "slang" for "a solution to the line
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by npp 14y ago
One way would be to take a course on a topic that uses linear algebra heavily, rather than taking a generic linear algebra course. Since all the ideas from linear algebra are then being used to actually do something, you'll get at least one
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by npp 15y ago
It's a good followup. Those models are widely used in computer vision, natural language processing, and other AI and applied ML areas, so those classes all go together.
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by npp 16y ago
In an earlier comment, you said that much of the time was spent in constructing the features (e.g. you had to implement CSS). Did you mean implementation time, or training/classification time? This latest comment makes it sound like most of
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by npp 16y ago
Some higher-level math will be important, other parts will not be. The parts that will be more useful are more on the analysis side (real analysis, complex analysis, functional analysis, convex analysis, Fourier analysis, probability theory
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by npp 16y ago
People have already explained the fact that the field now goes under a number of different names, and that it is very active, so I won't rehash this. A few pointers to things where you can keep track that are different from what others sugg
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by npp 16y ago
Advanced Linear Algebra, Roman; Linear Algebra, Hoffman & Kunze; Matrix Analysis, Horn & Johnson; Principles of Mathematical Analysis, Rudin; Real Analysis, Royden. Miscellaneous comments: - Reading pure abstract algebra (e.g. Dummi
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by npp 16y ago
One could say a number of things, but I'll just say this: I would suggest that you consider working for a bit in between undergrad and grad school. If you had a strong chance of getting in directly from undergrad, then it will remain so aft
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by npp 16y ago
I haven't read the GraphLab paper in detail, but note that Google itself developed a new framework called Pregel for doing graph-based data processing that doesn't fit MapReduce well. They have a paper on it at SIGMOD 2010. So there is defi
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by npp 16y ago
I'm just curious; how can you recommend it to family/friends looking for a computer , since currently you still need an extra machine to sync the iPad to? Is it that they have a desktop and wanted a laptop?
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by npp 16y ago
Some fairly well-known consumer-facing things that use machine learning are spam filters, recommendation engines, speech recognition systems (speech-to-text or customer service stuff), internet advertising, news clustering (Google News), re
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by npp 16y ago
One option is to browse some major university websites for commonly used textbooks and pick one that's appropriate for your level (in terms of math/econ background) and seems interesting. Some common choices are Krugman & Wells and book
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by npp 16y ago
To get some candidate schools, you may want to take a look at rankings (e.g. US News) for Ph.D. programs in computer science -- if you're actually sure you want to do CS, as people's tastes often change in college. The specific rankings are
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by npp 16y ago
Gruber does approach technology from a pro-Apple viewpoint, but many of his articles are at least organized around verifiable facts, though some may argue with his opinions or conclusions. In most (though not all) Gruber articles, it is at
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by npp 16y ago
Sorry if this is off topic, but am just curious -- what does a "good bible program" involve? Surely the Bible is available via some kind of Kindle/iBook thing?
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by npp 16y ago
People keep saying this, but the point is that Facebook got traction to begin with because it was highly private (only open to a small number of Ivy League colleges), and even later had strict privacy settings. Many people who joined never
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by npp 16y ago
Most banal example is intervals in the real line (e.g. reals between 0 and 1), which show up all the time. But comfort with infinity is needed for other uses also, two examples being convergence and asymptotic analysis of practical algorith
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by npp 16y ago
"Hard" is of course highly subjective, but the material in this article is standard (usually sophomore-junior) undergrad material and is very widely used.
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by npp 16y ago
I recall Microsoft, Google, and other large engineering companies making very similar efforts (and the tech companies certainly push lifestyle factors much more directly). It's the same in lots of areas, not just finance and consulting. The
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by npp 16y ago
Agreed, but this is kind of an "80-20 rule" thing. 80% of the time is spent on those 20% of activities, and 80% of people are basically just doing email, browsing, Twitter/Facebook, and media/entertainment. Even people who do more complex t