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>`_