My god! He's going to sue everyone who answers the Arc Challenge!

It's a trap!

Software Patents are a sham.

Software should be copyrighted, but not patented.

I wonder what would be if a writer could patent story lines: "It is like a like a novelist, patenting all action thrillers, that involve a guy saving a girl, who is kidnaped by the bad guys.

Watch out, any other books that have a bad guy kidnaping the girl, can be infringing this patent."

There are many industries that flourish without the needs of patents. How did we end up in this mess?

I'm in my late twenties, and even though I did comm systems as a EE undergrad (lots of math), I still don't feel like I really have a deep understanding on probability/stochastic processes, differential equations, and complex analysis.

www.betterexplained.com has some good tidbits for math.

I've found MIT's opencourseware to be a pretty good help: http://ocw.mit.edu/OcwWeb/web/courses/courses/index.htm#Math...

Only the undergraduate courses tend to have video lectures though. The ones on linear algebra and diff eq are quite good. When I first learned matricies in high school, the teachers just went through the mechanics of how to manipulate them and how to calculate a determinant. It wasn't until years later, and when I started wtching these lectures that it crystallized for me what it actually meant.

These are monthly lectures on math topics, which have been enlightening. http://www.ams.org/featurecolumn/index.html

If you like exploring, there's this: http://www.jimloy.com/math/math.htm

some free online texts http://www.math.gatech.edu/~cain/textbooks/onlinebooks.html

If you haven't had a course before, you can just follow the usual sequence of courses that high school students and college students take. calculus, multi-var calc, diff-eq, linear algebra, probabilty. And get a textbook and work through the problems. If you code, discrete math will probably help somewhere along the way. Probability/stat for machine learning.

If you've already had the stuff before, It might help just to pick one small topic in a math field, like gradients in multi-var calc, and just focus on that for a bit, and inevitably, it'll mention some other math tool that you don't know about, and just follow your nose and interests.

What I didn't learn until after I finished undergrad is that if you want to really understand conceptually what things mean in math, and not just how to manipulate symbols, there's no getting around working on problems paper (or matlab/mathematica) and just playing with it.

Hope that helps.

If you want fundmantal, intuitive understanding I suggest What is Mathematics? by Courant and Robbins. I got this after pg recommended it in an essay, and it's wonderful.

I'm sorry but it's bullshit. I taught myself programming with BASIC when I was a kid, my first programs were horrible, unstructured things with GOTOs everywhere and redundant code and yet I was able to teach myself Scheme as well a few years later.

In fact, I find that I understand the point behind useful programming language constructs (and, yes, their beauty) after spending some time suffering due to the lack of them.

This might sound silly - but I've found the best way to learn anything about math is to start w/ Wikipedia.

Search for a topic you are interested in, like Calculus. Start there. Spend a few hours reading and clicking through links, finding books that are cited, etc. If you don't understand something, usually some link will have the background information you need.

Do this every week or so.

"But, I now have a burning desire to learn it from the ground-up. What are the 'canonical' sources for math, both online and offline?"

It'd be easy to spend multiple lifetimes studying math, so you'll have to set some priorities. Applied vs. pretty, pragmatic vs. rigorous, discrete vs. continuous, and various subfields within "applied," e.g. So presently, when you have a better idea what your priorities are, you'll probably want to pose a variant of the question again.

(E.g., not "what are the 'canonical' sources for math" but something as specific as "what are the 'canonical' sources for math leading up to what I'd need to understand X" where X is something like "the cryptanalysis of the Data Encryption Standard" or "the proof of Fermat's last theorem [good luck:-]" or "why people think Y's work was important" where Y is Galois or Hilbert or Ramanujan or Noether or Erdos or Matiyasevic or whoever.)

Meanwhile, if you just want to see what the fuss is about before trying to formulate a more specific question, I can recommend any of four kinds of samplers.

1. For about 80-90% of ways of analyzing the physical world, one really wants to know calculus. Get _A Concept of Limits_ (cheap from Dover), the three most promising calculus books from your local library (and/or webbed tutorials), and a basic dealing-with-the-physical-world book which assumes you know calculus (e.g., just about any serious physics text, or _The Art of Electronics_, or something acoustics or signal processing or whatever). Keep fiddling with them, and doing exercises as necessary, 'til the pieces fit together.:-| Expect it to be quite a lot of work --- by my estimate, freshmen and sophomores at Caltech in the 1980s generally spent at least 250 hours on it, sometimes more like 1000. And it will probably be much easier if, like them, you can arrange to get at least 1 hour of feedback every 20 hours of study from someone who already understands the stuff.

2. For anything in computers, getting familiar with the basic math of reasonably serious algorithms is really useful. I, like many people, like _Introduction to Algorithms_. Get it and study it; understand at least a representative number of chapters. My estimate is that this is a lot easier than option #1, maybe five times easier. It isn't anywhere near as big a hammer for dealing with the physical world, but it can be extremely handy for dealing with the software world.

3. If you want to see what all the fuss is about in some representative areas of less-physical, less-computer-y math, I know of two Dover books which try to drag you from advanced high school math to a famous math result. _Abstract Algebra and Solution by Radicals_ drags you through (the modern, cleaned up and rigorous version of) Galois' proof that there is no closed-form formula for solving polynomials of fifth order. _Computability and Unsolvability_ drags you up to Matiyasevic's proof that Hilbert's tenth problem is insoluble. Working through either of them in detail would be a lot of work, almost certainly more than you want to do if your interest turns out to lie in something else like graph theory or algorithms or topology or statistics. But you could probably learn a lot about roughly how things are done merely by skimming either of them a few times. (And if just seeing broadly how things are done is your priority, you might prefer _AAaSbR_, since showing broadly how things are done seems to be one of its priorities too.)

4. Peter Winkler's newish (2004) _Mathematical Puzzles_ book is also very good and very math-y and well worth looking at as a sort of inspiration. However, if you ever get tempted to think that the extreme elegance of puzzle solutions is representative of how math gets done, look back at section 3 before jumping to conclusions.

"I am lost as to where I should start. I want to have a fundamental, intuitive understanding of it."

My closest thing to a literal answer to that would be: read _AAaSbR_. Like it very, very much.:-) Like it so much, in fact, that you are motivated to really study something like _Algebra_ by MacLane and Birkhoff (which is like a big watershed in which _AAaSbR_ is but one stream). After you get your mind around a good chunk of that (enough that you feel no great fear of an open-book exam composed of exercises from your choice of 20% of the chapters, say), do some variant of the calculus stuff I described in section 1 to see how abstract math ties into the stuff people analyze in the physical world. But I doubt in fact this is what you want. I suspect it'd be more than a full-time year of work for most people. And even if you had the time and energy, well before you finished I think you'd probably prefer to stop studying the foundational stuff so deeply and start to climb up some shortcut to some application or subspecialty.

Incidentally, mooneater's advice "algebra [...] Be very comfy with that before proceeding" is good... but note that it's referring to a high school algebra which has rather different priorities from something like what MacLane and Birkhoff mean. Don't try to follow mooneater's advice by going to a university library, taking down a book titled "Algebra," and running away screaming "math is not for me." I learned a lot of useful math, did my Ph. D. on quantum mechanical Monte Carlo simulations, and only understand a little of MacLane and Birkhoff (but have looked parts of it in order to try to understand a little bit about "categories" and some other stuff, and would consider more time spent understanding it to be time well spent).

Steve Yegge says some interesting things about various branches of maths and how they relate to programming:

http://steve-yegge.blogspot.com/2006/03/math-for-programmers...

where is the mod down button.

This is about the store not the version control

Bob Metcalfe: http://tinyurl.com/3yqsb6

"1. Selling Matters

I have a six-story townhouse in Boston overlooking MIT on the Charles River. I often invite young engineers and would-be entrepreneurs over to schmooze. Many of them tell me my townhouse is beautiful and they hope to invent something like Ethernet that will get them such a house.

The picture they have in their heads is of me lounging around on the beanbag chairs in a conference room at Xerox PARC in 1973. They see me having this idea for a computer network and submitting it as an invention proposal to Xerox. Then they envision me putting my feet up and letting the royalties roll in until I have enough to come up with the down payment on the townhouse with the river view.

My picture-the actual picture-is different. It's the picture of innovation rather than invention, the weed instead of the flower. In my picture it's the dead of winter and I am in the dark in a Ramada Inn in Schenectady, New York. A telephone is ringing with my wake-up call at 6 a.m., which is 3 a.m. in California, where I flew in from last night. I don't know yet where I am, or where that damn ringing is coming from, but within the hour I'll be in front of hostile strangers selling them on me, my company, and its strange products, which they have no idea they need.

If I persist, selling like this for 10 years, and I do it better and better each time, and I build a team to do everything else better and better each time, then I get the townhouse. Not because of any flowery flash of genius in some academic hothouse.

Most engineers don't understand that selling matters. They think that on the food chain of life, salespeople are below green slime. They don't understand that nothing happens until something gets sold. The way I think about it is that there are three sets of people in the world. There is the set of people who will buy your products no matter what (think of your mother). There's the set who will never buy your products (think of your competitors). Both are much smaller than the set of people who will buy your products if the products are competently sold to them. That vast middle set is why sales is so important, and it represents one of the key differences between invention, which comes up with a brilliant new idea, and innovation, which gets that inspiration out into the world.

Sales may not matter in invention, but it matters-in a very big way-in innovation."

I will eventually, but first I have to abstract out all the news.yc-specific stuff.

The book 'Calculus' by Michael Spivak.

In the same way that SICP transforms you from a high-schooler into a wise adult when it comes to programming, so too does Calculus when it comes to maths. If you find the book to be heavy going, then read whatever preliminary material you need, and go back to it.

Edit: I should also stress that maths requires a fair amount of discipline (a lot more than programming), so it's really hard to study maths while also having a day job.

PHP:

  if ($S['foo']) echo "you said {$S['foo']}";
  elseif (PtoS('foo')) link('said','click here');
  else form('foo');

For me, making programs short is not a design goal. But if it would, I would do it like above. My framework then would include this functionality:

  S[] contains all session variables
  P[] contains all posted variables
  PtoS copies an entry from P to S. Returns false if P was not set.
  link() prints a link
  form() shows a form with the given fields. target is the current url.

Someone downmodded you for simply asking a question. I didn't know we had Ubuntu nazis here...

The answer is yes and no. I cannot stand KDE because it reminds me of Winodws too much. Gnome is more "authentic" - sure there are close/maximize/minimize buttons, and hotkeys are the same. Gnome is much younger than Windows and picking up established standards for keys wasn't a bad idea. One of the reasons I went with FireFox instead of Opera some time ago was because FireFox had all the hotkeys I was using with IE.

However, under the surface Gnome is very different from Win. For instance windows can have two additional states: "vertically maximized" and "horizontally maximized" - and there are hotkeys for both. Additionally there are many little nice gems, like you can drag your windows around without use of the title bar, just hold Alt key. Virtual desktops are also tightly integrated, you can switch between the two and easily throw windows between them.

I also love the menu bar at the top: it has everything on it. By everything I mean literally "everything I want to see in front of my eyes at all times": status of hardware, weather, shortcuts, etc.

But the real "bomb" IMO is Ubuntu's font rendering. On high DPI screens the comfort of reading text is approaching that of paper. Granted, I am using non-standard libcairo (font rendering) package and my settings are adjusted from Ubuntu's defaults, but the point is that it's there: and going back to Windows makes me feel crippled and tired after a while. I end up taking my Ubuntu laptop to work and use it for web browsing and email, anything to take a break from thin and low-contrast Windows fonts.