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mailshanx
searching PlanetScale…
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43 ms
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151.
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mailshanx
14y ago
>>In fact, I lived in the UK for seven years: in that time, I made as much progress in my tech career as I did in my first six months in the US. Can you elaborate on that?
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mailshanx
14y ago
One a related note: Aaron Swartz wrote an article series he calls "raw nerve". One of those articles expands on Carol Dweck's work: http://www.aaronsw.com/weblog/dweck
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mailshanx
14y ago
Interesting! Can i subscribe to your feed?
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mailshanx
14y ago
Linux Mint 14 Cinnamon, with xmonad as window manager
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mailshanx
14y ago
-Submit my thesis and get my Masters -Publish my masters work in the leading IEEE conference in my area. -Move to Europe to live and work there -Learn more CS, particularly in the areas of natural language processing and distributed computi
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mailshanx
14y ago
Could you please elaborate on your programming journey? I'd be very interested to know.
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mailshanx
14y ago
Oh man, that's an awesome government indeed! May i ask which nation you hail from?
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mailshanx
14y ago
Thats intriguing. It seems like he has somewhat adjusted over time. Did you witness a general improvement in his personality as he grew up? Did things especially improve in his late teens perhaps?
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mailshanx
14y ago
An awesome gesture. Happy thanksgiving from Singapore!:)
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mailshanx
14y ago
Thanks for the excellent explanation, and many congratulations on your thesis!:) Could you point to any resources on time series analysis? While i am well familiar with supervised/unsupervised learning methods for tasks like classification,
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mailshanx
14y ago
Hey Daniel i've emailed you a lawyer contact.
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mailshanx
14y ago
Hey PG, would you regard climbing a ~20,000 ft Himalayan mountain (~single digit known previous attempts) as an impressive achievement?
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mailshanx
14y ago
I would reserve GAs (at least in their naive form) as a last-resort option. They are extremely inefficient solutions because it doesn't exploit any structure in the problem. Essentially, a GA solution consists of: 1. Select a random point(s
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mailshanx
14y ago
Awesome read, thanks for taking the time to write it :)
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mailshanx
14y ago
I find that at a practical level, linear algebra and probability/stochastic processes are the most valuable and heavily used topics in machine learning. For Linear algebra, i'd recommend Gilbert Strang's book+his MIT OCW lectures. Check out
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mailshanx
14y ago
Really? I find it hard to understand why <em>anyone</em> would consider the idea of life extension as taboo. Wouldn't it be safe to assume that almost everyone, irrespective of your religious or other inclinations, desire to
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mailshanx
14y ago
Usually, algorithms/data structures are the second course in a CS sequence. If you can code simple programs, you need nothing more than high-school math to be able to work through Skiena's book. Other than that, i would suggest practicing o
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mailshanx
14y ago
Can you elaborate?
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The laziness paradox
(scotthyoung.com)
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mailshanx
14y ago
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Top 10 most read books in the world
(visual.ly)
2 points
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mailshanx
14y ago
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mailshanx
15y ago
I think this is pretty accurate. Here is an example from my own thesis research: I'm using machine learning to tune an (underwater) communication link, i.e. decide what modulation / error coding algorithms/parameters will yield good data ra
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mailshanx
15y ago
Im 25. I remember my dad mentioning writing software to compute the trajectory of artillery shells when he was a Navy cadet. He later went on to establish some of the early IT institutes the Navy uses to train it's personel (this was some t
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mailshanx
15y ago
Holy cow, after reading some of the comments, i just realized that I too am a second generation programmer! My dad wrote some software to compute artillery trajectories while he was a cadet in the Navy. He later went on to establish some of
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mailshanx
15y ago
Digital communications by Bernard Sklar. A close second is Wayne Tomasi's book. Both books provide great intuition into the matter with just about the right detail. Once you have both of those down (and have the requisite linear algebra / p