5 ms·
This is because, when each machine updates the master server, the update tends to be additive. Hmm, reminds me of GFS (append-only file system). It seems like
by Locke1689 12y ago
This is because, when each machine updates the master server, the update tends to be additive.
Hmm, reminds me of GFS (append-only file system). It seems like I managed to really short my machine learning education during my degrees. Anyone have any recommendations for good introductory resources for practical machine learning design and implementation?
- fenomas 12y agoI did Stanford's online ML course (taught by Andrew Ng, quoted in the article), and heartily recommend it. I found it to be a nice balance of theory and practice - it's also not a bad way to learn Octave (the GNU equivalent of matlab) if one wants to do that. http://online.stanford.edu/course/machine-learning http://online.stanford.edu/course/machine-learning (I don't know if there's a way to just access the materials and videos though, rather than signing up for the course.)
- dekhn 12y agoYou're conflating the append operation with (in their example) addition, which is completely different. Append makes a list longer. Addition combines two elements in a reduction. Addition is commutative. And the other issue here is that learning is tolerant of a small amount of noise. You don't want your filesystem of record to be.
- Locke1689 12y agoI'm not actually. They both share the commonality of non-destructive updates, which seems to be a recently pervasive technique of performing unsynchronized parallel work. I do grant that the techniques are not identical, though. Certainly GFS has at least minor locking so that it can grab the current high water mark.