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[REVIEW] link Logisitc MNMG via dask-glm demo to readme #3151

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merged 7 commits into from
Nov 23, 2020
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[REVIEW] link Logisitc MNMG via dask-glm demo to readme #3151

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Merge pull request #15 from rapidsai/branch-0.15
daxiongshu Jul 26, 2020
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Merge pull request #18 from rapidsai/branch-0.17
daxiongshu Nov 1, 2020
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Merge pull request #19 from rapidsai/branch-0.17
daxiongshu Nov 17, 2020
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add dask-glm demo link
daxiongshu Nov 17, 2020
26a6715
add to changelog
daxiongshu Nov 18, 2020
e4f522d
Merge branch 'branch-0.17' into link_dask_glm_to_readme
cjnolet Nov 18, 2020
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Merge branch 'branch-0.17' into link_dask_glm_to_readme
dantegd Nov 22, 2020
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -96,7 +96,7 @@ repo](https://github.com/rapidsai/notebooks-contrib).
| **Linear Models for Regression or Classification** | Linear Regression (OLS) | Multi-node multi-GPU via Dask |
| | Linear Regression with Lasso or Ridge Regularization | Multi-node multi-GPU via Dask |
| | ElasticNet Regression | |
| | Logistic Regression | |
| | Logistic Regression | Multi-node multi-GPU via Dask-GLM [demo](https://github.com/daxiongshu/rapids-demos) |
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Do you have any plans to push this notebook & readme to a RAPIDS repository? It would also be useful for users to provide a link to the Dask-GLM logistic regression docs / repo.

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I was trying to push the notebook to notebooks-contrib but it seems that repo is under some changes so @JohnZed suggested to link my repo for now.

I'll add the link to dask-glm and documents in the readme of my repo. Thank you!

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Yes, I support leaving it in a personal repo with a readme now until reorganizations in notebooks-contrib etc. are finalized so we don't have to keep moving around the link.

| | Naive Bayes | Multi-node multi-GPU via Dask |
| | Stochastic Gradient Descent (SGD), Coordinate Descent (CD), and Quasi-Newton (QN) (including L-BFGS and OWL-QN) solvers for linear models | |
| **Nonlinear Models for Regression or Classification** | Random Forest (RF) Classification | Experimental multi-node multi-GPU via Dask |
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