rapidsai-community · rnyak · Jan 30, 2020 · Apr 6, 2020
diff --git a/README.md b/README.md
@@ -48,7 +48,6 @@ Below is a listing of the notebooks in this repository.  Each row will tell you
 | basics   | [hello_streamz](getting_started_notebooks/basics/hello_streamz.ipynb)  | This notebook demonstrates use of cuDF to perform streaming word-count using a small portion of the Streamz API. | SG | Self Generated |     
 |basics -> blazingsql| [Getting Started with BlazingSQL](getting_started_notebooks/basics/blazingsql/blazingsql_demo.ipynb) | How to set up and get started with BlazingSQL and the RAPIDS AI suite. | SG | [Music Dataset](https://github.com/BlazingDB/bsql-demos/blob/master/data/Music.csv) |
 |basics -> blazingsql| [Federated Query Demo](getting_started_notebooks/basics/blazingsql/federated_query_demo.ipynb) | In a single query, join an Apache Parquet file, a CSV file, and a GPU DataFrame (GDF) in GPU memory. | SG | [Breast Cancer Diagnostic](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) |
-| SG | Self Generated |
 | intro_tutorials   | [01_Introduction_to_RAPIDS](getting_started_notebooks/intro_tutorials/01_Introduction_to_RAPIDS.ipynb)  | This notebook shows at a high level what each of the packages in RAPIDS are as well as what they do.                                                                                                                                      |  MG | Self Generated |
 | intro_tutorials   | [02_Introduction_to_cuDF](getting_started_notebooks/intro_tutorials/02_Introduction_to_cuDF.ipynb)  | This notebook shows how to work with cuDF DataFrames in RAPIDS.                                                                                                                                      | SG | Self Generated |
 | intro_tutorials   | [03_Introduction_to_Dask](getting_started_notebooks/intro_tutorials/03_Introduction_to_Dask.ipynb)   | This notebook shows how to work with Dask using basic Python primitives like integers and strings.                                                                                                                                      |  MG | Self Generated |
@@ -93,7 +92,7 @@ Below is a listing of the notebooks in this repository.  Each row will tell you
 |-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----|
 | cyber -> flow_classification     | [flow_classification_rapids](blog_notebooks/cyber/flow_classification/flow_classification_rapids.ipynb)              | **Archive Only.**  The `cyber` folder contains the associated companion files for the blog [GPU Accelerated Cyber Log Parsing with RAPIDS](https://medium.com/rapids-ai/gpu-accelerated-cyber-log-parsing-with-rapids-10896f57eee9), by Bianca Rhodes US, Bhargav Suryadevara, and Nick Becker.  This notebook demonstrates how to load netflow data into cuDF and create a multiclass classification model using XGBoost. Uses [run_raw_data_generator](blog_notebooks/cyber/raw_data_generator/run_raw_data_generator.py)                                                                                                    | SG | [University of New South Wales LanL Dataset](https://iotanalytics.unsw.edu.au/) |
 | cyber -> network_mapping      | [lanl_network_mapping_using_rapids](blog_notebooks/cyber/network_mapping/lanl_network_mapping_using_rapids.ipynb)               | **Archive Only.**  The `cyber` folder contains the associated companion files for the blog [GPU Accelerated Cyber Log Parsing with RAPIDS](https://medium.com/rapids-ai/gpu-accelerated-cyber-log-parsing-with-rapids-10896f57eee9), by Bianca Rhodes US, Bhargav Suryadevara, and Nick Becker.  This notebook demonstrates how to parse raw windows event logs using cudf and uses cuGraph's pagerank model to build a network graph. Uses [run_raw_data_generator](blog_notebooks/cyber/raw_data_generator/run_raw_data_generator.py)                                                                        |  SG | [University of New South Wales LanL Dataset](https://iotanalytics.unsw.edu.au/) |
-| databricks   | [RAPIDS_PCA_demo_avro_read](blog_notebooks/databricks/RAPIDS_PCA_demo_avro_read.ipynb)              | The `databricks` folder is the companion file repository to the blog [RAPIDS can now be accessed on Databricks Unified Analytics Platform](https://medium.com/rapids-ai/rapids-can-now-be-accessed-on-databricks-unified-analytics-platform-666e42284bd1) by Ikroop Dhillon, Karthikeyan Rajendran, and Taurean Dyer.  This notebooks purpose is to showcase RAPIDS on Databricks use their sample datasets and show the CPU vs GPU comparison for the PCA algorithm. There is also an accompanying HTML file for easy Databricks import.  **This notebook is for illustrative purposes only!  Do not expect this notebook to successfully run on its own- this notebook's code is replicates a workflow meant to run on a specific platform, `Databricks`**  | SG | [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)|                                                     
+| databricks   | [RAPIDS_PCA_demo_avro_read](blog_notebooks/databricks/RAPIDS_PCA_demo_avro_read.ipynb)              | The `databricks` folder is the companion file repository to the blog [RAPIDS can now be accessed on Databricks Unified Analytics Platform](https://medium.com/rapids-ai/rapids-can-now-be-accessed-on-databricks-unified-analytics-platform-666e42284bd1) by Ikroop Dhillon, Karthikeyan Rajendran, and Taurean Dyer.  This notebooks purpose is to showcase RAPIDS on Databricks use their sample datasets and show the CPU vs GPU comparison for the PCA algorithm. There is also an accompanying HTML file for easy Databricks import.  **This notebook is for illustrative purposes only!  Do not expect this notebook to successfully run on its own- this notebook's code is replicates a workflow meant to run on a specific platform, `Databricks`**  | SG | [RAPIDS Toy Dataset](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)|                                                     
 | plasticc-> notebooks   | [rapids_lsst_full_demo](blog_notebooks/plasticc/notebooks/rapids_lsst_full_demo.ipynb)               |   **Archive Only.** This notebook demos the full CPU and GPU implementation of the RAPIDS.ai team's model that placed 8/1094 in the PLAsTiCC Astronomical Classification competition.  [Blog](https://medium.com/rapids-ai/make-sense-of-the-universe-with-rapids-ai-d105b0e5ec95).  [Updated notebooks found here](conference_notebooks/KDD_2019/plasticc/)   | MG | [Kaggle PLAsTiCC-2018 dataset](https://www.kaggle.com/c/PLAsTiCC-2018/data) |
 | plasticc-> notebooks   | [rapids_lsst_gpu_only_demo](blog_notebooks/plasticc/notebooks/rapids_lsst_gpu_only_demo.ipynb)               |  **Archive Only.** This GPU only based notebook shows the RAPIDS speedup of the the RAPIDS.ai team's model that placed 8/1094 in the PLAsTiCC Astronomical Classification competition.  [Blog](https://medium.com/rapids-ai/make-sense-of-the-universe-with-rapids-ai-d105b0e5ec95).  [Updated notebooks found here](conference_notebooks/KDD_2019/plasticc/) | MG | [Kaggle PLAsTiCC-2018 dataset](https://www.kaggle.com/c/PLAsTiCC-2018/data) |
 | santander   | [cudf_tf_demo](blog_notebooks/santander/cudf_tf_demo.ipynb)               |  **Archive Only.** This financial industry facing notebook is the cudf-tensorflow approach from the RAPIDS.ai team for Santander Customer Transaction Prediction.   Placed 17/8808. [Blog](https://medium.com/rapids-ai/financial-data-modeling-with-rapids-5bca466f348)   | SG | [Kaggle Santander Customer Transaction Prediction Dataset]( https://www.kaggle.com/c/santander-customer-transaction-prediction/data)