- The cluster parallel learning code better supports multiple simultaneous runs, and other forms of parallelism have been mostly removed. This incidentally significantly simplifies the learning core.
- The online learning algorithms are more general, with support for l1 (via a truncated gradient variant) and l2 regularization, and a generalized form of variable metric learning.
- There is a solid persistent server mode which can train online, as well as serve answers to many simultaneous queries, either in text or binary.
This should be a very good release if you are just getting started, as we’ve made it compile more automatically out of the box, have several new examples and updated documentation.
- Miro will cover the L-BFGS implementation, which he created from scratch. We have found this works quite well amongst batch learning algorithms.
- Alekh will cover how to do cluster parallel learning. If you have access to a large cluster, VW is orders of magnitude faster than any other public learning system accomplishing linear prediction. And if you are as impatient as I am, it is a real pleasure when the computers can keep up with you.
This will be recorded, so it will hopefully be available for viewing online before too long.
I hope to see you soon