Both CNTK and Vowpal Wabbit have pirate tutorials at NIPS. The CNTK tutorial is 1 hour during the lunch break of the Optimization workshop while the VW tutorial is 1 hour during the lunch break of the Extreme Multiclass workshop. Consider dropping by either if interested.

CNTK is a deep learning system started by the speech people who started the deep learning craze and grown into a more general platform-independent deep learning system. It has various useful features, the most interesting of which is perhaps efficient scalable training. Using GPUs with allreduce and one-bit sgd it achieves both high efficiency and scalability over many more GPUs than could ever fit into a single machine. This capability is unique amongst all open deep learning codebases so everything else looks nerfed in comparison. CNTK was released in April so this is the first chance for many people to learn about it. See here for more details.

The Vowpal Wabbit tutorial just focuses on what is new this year.

- The learning to search framework has greatly matured and is now easily used to solve ad-hoc joint(structured) prediction problems. The ICML tutorial covers algorithms/analysis so this is about using the system.
- VW has also become the modeling element of a larger
*system*(called the decision service) which gathers data and uses it as per Contextual Bandit learning. This is now generally usable, and is the first general purpose system of this sort.

Was this tutorial taped or are the slides available as .pdf somewhere?

For CNTK, I’m not sure. For the VW tutorial, it’s here: https://github.com/JohnLangford/vowpal_wabbit/wiki/Tutorial

[…] CNTK and Vowpal Wabbit tutorials at NIPS Both CNTK and Vowpal Wabbit have pirate tutorials at NIPS. The CNTK tutorial is 1 hour during the lunch break of the Optimization workshop while the VW tutorial is 1 hour during the lunch break of the Extreme Multiclass workshop. Consider dropping by either if interested. CNTK is a deep learning system started… Original post: CNTK and Vowpal Wabbit tutorials at NIPS […]