new stuff

CHANGELOG.md

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+# Notebooks 0.8.0 (27 June 2019)
+
+## New Features
+- Each notebook is linked to in README.md
+- PR #178 Adding KMeans notebook
+- PR #18 Added cuDF example notebooks
+
+## Improvements
+- Updated SSSP notebook
+- Regression notebooks show of `train_test_split()` functon
+
+## Bug Fixes
+
+
+# Notebooks 0.7.0 (10 May 2019)
+
+## New Features
+- PR #133 Adding cuGraph notebooks
+
+## Improvements
+- PR #144  Added top level CHANGELOG.  Added a README to cugraph  
+
+## Bug Fixes
+
+
+# Notebooks 0.6.0 (22 Mar 2019)
+
+
+

README.md

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 # RAPIDS Notebooks and Utilities
 
-* `cuml`: contains four example notebooks showing the usage of different machine learning algorithms included in cuML: `knn`, `dbscan`, `pca` and `tsvd`. It also includes a small subset of the Mortgage Dataset used in the notebooks.
-* `mortgage`: contains the notebook which runs ETL + ML on the Mortgage Dataset derived from [Fannie Mae’s Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) ... download the mortgage dataset for use with the notebook [here](https://rapidsai.github.io/demos/datasets/mortgage-data)
+## XGBoost Notebook
+| Folder    | Notebook Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| XGBoost   | [XGBoost Demo](xgboost/XGBoost_Demo.ipynb)           | This notebook shows the acceleration one can gain by using GPUs with XGBoost in RAPIDS.                                                                                                                                       |
+## CuML Notebooks
+ The cuML notebooks showcase how to use the machine learning algorithms implemented in cuML along with the advantages of using cuML over scikit-learn. These notebooks compare the time required and the performance of the algorithms. Below are a list of such algorithms:
+
+| Folder    | Notebook Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| cuML      | [dbscan_demo](cuml/dbscan_demo.ipynb)            | This notebook showcases density-based spatial clustering of applications with noise (dbscan) algorithm using the `fit` and `predict` functions                                                                              |
+| cuML      | [knn_demo](cuml/knn_demo.ipynb)               | This notebook showcases k-nearest neighbors (knn) algorithm using the `fit` and `kneighbors` functions                                                                                                                          |
+| cuML      | [Linear Regression Demo](cuml/linear_regression_demo.ipynb) | This notebook includes code example for linear regression algorithm and it showcases the `fit` and `predict` functions.                                                                                                                                             |
+| cuML      | [Ridge Regression Demo](cuml/ridge_regression_demo.ipynb)  | This notebook includes code examples of ridge regression and it showcases the `fit` and `predict` functions.                                                                                                                                          |
+| cuML      | [Coordinate Descent](cuml/coordinate_descent_demo.ipynb)     | This notebook includes code examples of lasso and elastic net models. These models are placed together so a comparison between the two can also be made in addition to their sklearn equivalent.                                                                                                                                                                |
+| cuML      | [pca_demo](cuml/pca_demo.ipynb)               | This notebook showcases principal component analysis (PCA) algorithm where the model can be used for prediction (using `fit_transform`) as well as converting the transformed data into the original dataset (using `inverse_transform`).                                                                                                                |
+| cuML      | [tsvd_demo](cuml/tsvd_demo.ipynb	)              | This notebook showcases truncated singular value decomposition (tsvd) algorithm which like PCA performs both prediction and transformation of the converted dataset into the original data using `fit_transform` and `inverse_transform` functions respectively                                                                                                     |
+| cuML      | [sgd_demo](cuml/sgd_demo.ipynb)               | The stochastic gradient descent algorithm is demostrated in the notebook using `fit` and `predict` functions                                                                        |
+| cuML      | [umap_demo](cuml/umap_demo.ipynb)              | The uniform manifold approximation & projection algorithm is compared with the original author's equivalent non-GPU \Python implementation using `fit` and `transform` functions                       |
+| cuML      | [umap_demo_graphed](cuml/umap_demo_graphed.ipynb)      | Demonstration of cuML uniform manifold approximation & projection algorithm's supervised approach against mortgage dataset and comparison of results against the original author's equivalent non-GPU \Python implementation. |
+| cuML      | [umap_demo_supervised](cuml/umap_supervised_demo.ipynb)   | Demostration of UMAP supervised training.  Uses a set of labels to perform supervised dimensionality reduction. UMAP can also be trained on datasets with incomplete labels, by using a label of "-1" for unlabeled samples. |
+| cuML      | [random forest](cuml/rf_demo.ipynb)   | This notebook includes code examples of Random Forest and it showcases the `fit` and `predict` functions. |
+
+## CuDF Notebooks
+| Folder    | Notebook Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| cuDF      | [notebooks_Apply_Operations_in_cuDF](cudf/notebooks_Apply_Operations_in_cuDF.ipynb)            | This notebook showcases two special methods where cuDF goes beyond the Pandas library: apply_rows and apply_chunk functions. They utilized the Numba library to accelerate the data transformation via GPU in parallel.                                                                            |
+| cuDF      | [notebooks_numba_cuDF_integration](cudf/notebooks_numba_cuDF_integration.ipynb)               | This notebook showcases how to use Numba CUDA to accelerate cuDF data transformation and how to step by step accelerate it using CUDA programming tricks                                                                                                                          |
+
+## CuGraph Notebooks
+| Folder    | Notebook Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| cuGraph   | [Louvain](cugraph/Louvain.ipynb)                | Demonstration of using cuGraph to identify clusters in a test graph using the Louvain algorithm                                                                                                                               |
+| cuGraph   | [Vertex-Similarity](cugraph/Vertex-Similarity.ipynb)      | Demonstration of using cuGraph to compute vertex similarity using both the Jaccard Similarity and the Overlap Coefficient.                                                                                                    |
+| cuGraph   | [Weighted-Jaccard](cugraph/Weighted-Jaccard.ipynb)       | Demonstration of using cuGraph to compute the Weighted Jaccard Similarity metric on our training dataset.                                                                                                                     |
+| cuGraph   | [Renumber](cugraph/Renumber.ipynb)               | Demonstrate of using the renumbering features to assigned new vertex IDs to the test graph.  This is useful for when the data sets is  non-contiguous or not integer values                                                   |
+| cuGraph   | [BFS](cugraph/BFS.ipynb)                    | Demonstration of using cuGraph to computer the Bredth First Search space from a given vertex to all other in our training graph                                                                                               |
+| cuGraph   | [SSSP](cugraph/SSSP.ipynb)                   | Demonstration of using cuGraph to computer the The Shortest Path from a given vertex to all other in our training graph                                                                                                       |
+| cuGraph   | [Spectral-Clustering](cugraph/Spectral-Clustering.ipynb)    | Demonstration of using cuGraph to identify clusters in a test graph using Spectral Clustering using both the (A) Balance Cut and (B) the Modularity Maximization quality metrics                                              |
+| cuGraph   | [Pagerank](cugraph/Pagerank.ipynb)               | Demonstration of using both NetworkX and cuGraph to compute the PageRank of each vertex in our test dataset                                                                                                                   |
+| cuGraph   | [Triangle Counting](cugraph/Triangle-Counting.ipynb)      | Demonstration of using both NetworkX and cuGraph  to compute the the number of Triangles in our test dataset                                                                                                                  |
+
+## Tutorial with an End to End workflow
+
+| Folder    | Notebook Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Tutorials | [DBSCAN_demo_full](tutorials/DBSCAN_Demo_Full.ipynb)       | Demonstration of how to use DBSCAN - a popular clustering algorithm - and how to use the GPU accelerated implementation of this algorithm in RAPIDS.                                                                               |
+
+## Utils Scripts
+| Folder    | Script Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Utils     | start-jupyter.sh       | starts a JupyterLab environment for interacting with, and running, notebooks                                                                                                                                                  |
+| Utils     | stop-jupyter.sh        | identifies all process IDs associated with Jupyter and kills them                                                                                                                                                             |
+| Utils     | dask-cluster.py        | launches a configured Dask cluster (a set of nodes) for use within a notebook                                                                                                                                                 |
+| Utils     | dask-setup.sh          | a low-level script for constructing a set of Dask workers on a single node                                                                                                                                                    |
+| Utils     | split-data-mortgage.sh | splits mortgage data files into smaller parts, and saves them for use with the mortgage notebook                                                                                                                              |
+
+## Documentation (WIP)
+| Folder    | Document Title         | Description                                                                                                                                                                                                                   |
+|-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Docs      | ngc-readme             |                                                                                                                                                                                                                               |
+| Docs      | dockerhub-readme       |                                                                                                                                                                                                                               |
+
+## Additional Information
+* The `cuml` folder also includes a small subset of the Mortgage Dataset used in the notebooks and the full image set from the Fashion MNIST dataset.
+
 * `utils`: contains a set of useful scripts for interacting with RAPIDS
+
+* For additional, community driven notebooks, which will include our blogs, tutorials, workflows, and more intricate examples, please see the [Notebooks Extended Repo](https://github.com/rapidsai/notebooks-extended)