Difference between revisions of "Class meeting for 10-605 Advanced topics for SGD"
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=== Optional Readings === | === Optional Readings === | ||
| − | * | + | * Agarwal, Alekh, et al. "A reliable effective terascale linear learning system." The Journal of Machine Learning Research 15.1 (2014): 1111-1133. |
=== What You Should Remember === | === What You Should Remember === | ||
Revision as of 10:53, 16 October 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
Slides
Optional Readings
- Agarwal, Alekh, et al. "A reliable effective terascale linear learning system." The Journal of Machine Learning Research 15.1 (2014): 1111-1133.
What You Should Remember
- How an AllReduce works and why it is more efficient than collecting and broadcasting parameters.
- Why adaptive, per-parameter learning rates can be useful.
- The general form for the Adagrad update.
- The dual form of the perceptron, as a kernel method
- Definitions: kernel, Gram matrix, RKHS
- How the "hash trick" can be formalized as a kernel
