Blogs and other references
==========================

The RAPIDS team blogs at https://medium.com/rapids-ai, and many of
these blog posts provide deeper dives into models or key features from
cuML. Here, we've selected just a few that are of particular interest
to cuML users:

Integrations, applications, and general concepts
------------------------------------------------

* `RAPIDS Configurable Input and Output Types <https://medium.com/@dantegd/e719d72c135b>`_
* `RAPIDS on AWS Sagemaker <https://medium.com/rapids-ai/running-rapids-experiments-at-scale-using-amazon-sagemaker-d516420f165b>`_

Tree and forest models
----------------------
* `Accelerating Random Forests up to 45x using cuML <https://medium.com/rapids-ai/accelerating-random-forests-up-to-45x-using-cuml-dfb782a31bea>`_
* `RAPIDS Forest Inference Library: Prediction at 100 million rows per second <https://medium.com/rapids-ai/rapids-forest-inference-library-prediction-at-100-million-rows-per-second-19558890bc35>`_
* `Sparse Forests with FIL <https://medium.com/rapids-ai/sparse-forests-with-fil-ffbb42b0c7e3>`_

Other popular models
--------------------
* `Accelerating TSNE with GPUs: From hours to seconds <https://medium.com/rapids-ai/tsne-with-gpus-hours-to-seconds-9d9c17c941db>`_
* `Combining Speed and Scale to Accelerate K-Means in RAPIDS cuML <https://medium.com/rapids-ai/combining-speed-scale-to-accelerate-k-means-in-rapids-cuml-8d45e5ce39f5>`_
* `Accelerating k-nearest neighbors 600x using RAPIDS cuML <https://medium.com/rapids-ai/accelerating-k-nearest-neighbors-600x-using-rapids-cuml-82725d56401e>`_

Academic Papers
---------------

* `Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (Sebastian Raschka, Joshua Patterson, Corey Nolet) <https://arxiv.org/abs/2002.04803>`_