deep learning
In 2006, after visiting the Computer History Museum’s exhibit on Chess, I opined: We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out. So.
algorithms
I graduated with a Computer Science minor from the University of Virginia in 1992. The reason it’s a minor and not a major is because to major in CS at UVa you had to go through the Engineering School, and I was absolutely not cut out for that kind
algorithms
One of my all time favorite blog entries is a truly epic tale of dating gone wrong that culminates in the strangest reference to P=NP you'll probably ever encounter. Joey: So you really did graduate from computer engineering? New Girl: Yes I did, from UBC! Joey: And
computer science
Have you ever heard a software engineer refer to a problem as "NP-complete"? That's fancy computer science jargon shorthand for "incredibly hard": The most notable characteristic of NP-complete problems is that no fast solution to them is known; that is, the time required to
file copy
If you've used Windows Vista, you've probably noticed that Vista's file copy performance is noticeably worse than Windows XP. I know it's one of the first things I noticed. Here's the irony-- Vista's file copy is based on
algorithms
In my previous post on shuffling, I glossed over something very important. The very first thing that came to mind for a shuffle algorithm is this: for (int i = 0; i < cards.Length; i++) { int n = rand.Next(cards.Length); Swap(ref cards[i], ref cards[n]); } It'
arrays
Pop quiz, hotshot. How would you write code to shuffle a deck of cards? I was thinking about this after reading Mike's card-shuffling algorithm woes: Here's where the non-CS mind comes into play. My first thought was to generate an unshuffled deck as an array-like structure