801 karma · joined October 10, 2013
As the exact target audience, it's kind of neat to me.
Also, by my reading, the problem is that the standard requires the nonce parameter to do something and Apple ignores it.
While those aren't great numbers, I personally think it's tolerable for most usages.
[0] https://www.typescriptlang.org/docs/handbook/release-notes/t...
This can be fixed by inserting this line after line 308 of euler_explorer.js, in the onpointerdown handler:
e.preventDefault();
In this case though, Elixir is still way lower, at somewhere around 30th compared to Go's 14th.
[1] https://redmonk.com/sogrady/2018/08/10/language-rankings-6-1...
I was able to find the article "What About a Simple Language? Analyzing the Difficulties in Learning to Program" by Mannila, et al., which found that first time programmers found it easier to write bug-free programs in Python than Java and subsequently had a lot less trouble with the details of Java after learning Python, as they did not have to use the mental bandwidth to learn the most basic concepts of programming while also learning static typing and the incantations required to use Java.
Say we have a curve that corresponds to how good of a fit your model is. We want to try to find the maximum on that curve. However, calculating every point of the curve is too expensive, so we want to minimize the number of points we have to check. So, we start with a guess as to the highest point on the curve and take the first and second derivatives of the curve at that point. This gives us enough to fit a parabola that approximates the curve in the neighborhood of our initial guess point. Then, it's pretty easy to solve for the highest point on the parabola. That's our new guess. Repeat that a few times until the guesses stop shifting much. If the curve is nicely shaped (i.e. smooth everywhere, only has one maximum), the guesses will converge on the highest point.
This is a often faster than a similar method, gradient ascent, which relies upon only taking the first derivative. This would yield a line in the vicinity of our guess, and then we just move our guess a little bit, such that it goes up the line. This is pretty slow, since it can't just go straight to a guess of the top, and if you go too fast, then it'll blow right past the maximum.
The Hessian matrix is the higher dimensional equivalent to the second derivative there and the gradient is the equivalent for the first derivative. For example, if we have a two dimensional surface in 3D, then those matrices will be 2x2 and capture the curvature of the 3D paraboloid in the vicinity of the guess. As you go up in dimensionality, they're called quadric hypersurfaces.
When you're fitting a logistic regression, your hypersurface is the logarithm of the likelihood that the data you have fits the logistic curve with parameters at that point. The logarithm makes the hypersurface better behaved and makes the calculus easier. You just need the gradient and the Hessian, evaluate those at your initial guess, fit a quadric hypersurface to the guess there, pop up to the top of that hypersurface, repeat a few times, and you've got your model.
That really just boils down to cryptocurrency being good for business models that are so new that they sound really risky. I'm not really sure that enabling such models is really a net good to society.
And if you don't profit from a website and just want to place advertisements to keep the lights on, you don't have that kind of money.
0.5 in base 10 is the same as 0.1 in base 2, which is the same as 0.11111... in base 3. And the choice of base is completely arbitrary, with some societies using others, like base 12 or base 60.