Difference between revisions of "Class meeting for 10-605 SGD and Hash Kernels"
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Stochastic gradient descent: | Stochastic gradient descent: | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/sgd.pptx Slides in Powerpoint] | + | * [http://www.cs.cmu.edu/~wcohen/10-605/2016/sgd.pptx Slides in Powerpoint] |
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/sgd.pdf Slides in PDF] | + | * [http://www.cs.cmu.edu/~wcohen/10-605/2016/sgd.pdf Slides in PDF] |
=== Quiz === | === Quiz === | ||
Revision as of 16:39, 1 August 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2015.
Slides
Stochastic gradient descent:
Quiz
Readings for the Class
Optional readings
- For logistic regression, and the sparse updates for it: Lazy Sparse Stochastic Gradient Descent for Regularized Multinomial Logistic Regression, Carpenter, Bob. 2008. See also his blog post on logistic regression. I also recommend Charles Elkan's notes on logistic regression (local saved copy).
- For hash kernels: Feature Hashing for Large Scale Multitask Learning, Weinberger et al, ICML 2009.
Things to Remember
- Approach of learning by optimization
- Optimization goal for logistic regression
- Key terms: logistic function, sigmoid function, log conditional likelihood, loss function, stochastic gradient descent
- Updates for logistic regression, with and without regularization
- Formalization of logistic regression as matching expectations between data and model
- Regularization and how it interacts with overfitting
- How "sparsifying" regularization affects run-time and memory
- What the "hash trick" is and why it should work
