Merge pull request rapidsai#1011 from rapidsai/branch-0.9

[gpuCI] Auto-merge branch-0.9 to branch-0.10 [skip ci]

CHANGELOG.md

@@ -62,6 +62,7 @@
 - PR #976: Update api.rst for new 0.9 classes
 - PR #973: Use cudaDeviceGetAttribute instead of relying on cudaDeviceProp object being passed
 - PR #978: Update README for 0.9
+- PR #1009: Fix references to notebooks-contrib
 
 ## Bug Fixes
 

README.md

@@ -10,7 +10,7 @@ programming. In most cases, cuML's Python API matches the API from
 
 For large datasets, these GPU-based implementations can complete 10-50x faster
 than their CPU equivalents. For details on performance, see the [cuML Benchmarks
-Notebook](https://github.com/rapidsai/notebooks-extended/blob/master/intermediate_notebooks/benchmarks/cuml_benchmarks.ipynb).
+Notebook](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/benchmarks/cuml_benchmarks.ipynb).
 
 As an example, the following Python snippet loads input and computes DBSCAN clusters, all on GPU:
 ```python
@@ -62,8 +62,8 @@ For additional examples, browse our complete [API
 documentation](https://docs.rapids.ai/api/cuml/stable/), or check out our
 introductory [walkthrough
 notebooks](https://github.com/rapidsai/notebooks/tree/master/cuml). Finally, you
-can find complete end-to-end examples in the [notebooks-extended
-repo](https://github.com/rapidsai/notebooks-extended).
+can find complete end-to-end examples in the [notebooks-contrib
+repo](https://github.com/rapidsai/notebooks-contrib).
 
 
 ### Supported Algorithms