Difference between revisions of "Class meeting for 10-605 SGD for MF"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/ | + | * [http://www.cs.cmu.edu/~wcohen/10-605/sgd-for-mf.pptx Matrix Factorization via SGD- Powerpoint] |
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/sgd-for-mf.pdf Matrix Factorization via SGD - PDF] | ||
=== Quiz === | === Quiz === | ||
| − | * [https://qna | + | * [https://qna.cs.cmu.edu/#/pages/view/67 Quiz for today] |
=== Papers Discussed === | === Papers Discussed === | ||
* [http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent], Gemulla et al, KDD 2011. | * [http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent], Gemulla et al, KDD 2011. | ||
| + | |||
| + | === Things to Remember === | ||
| + | |||
| + | * Definition of matrix factorization | ||
| + | * Common applications of matrix factorization, and how they map into the MF problem | ||
| + | * Loss functions for matrix factorization that are appropriate for collaborative filtering | ||
| + | * Algorithm and updates for SGD implementation of matrix factorization | ||
| + | * dSGD algorithm - what is done in parallel and what is done sequentially | ||
| + | * Definitions: stratum, interchangable steps, diagonal | ||
Latest revision as of 11:43, 19 October 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.
Slides
Quiz
Papers Discussed
- Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent, Gemulla et al, KDD 2011.
Things to Remember
- Definition of matrix factorization
- Common applications of matrix factorization, and how they map into the MF problem
- Loss functions for matrix factorization that are appropriate for collaborative filtering
- Algorithm and updates for SGD implementation of matrix factorization
- dSGD algorithm - what is done in parallel and what is done sequentially
- Definitions: stratum, interchangable steps, diagonal
