Home
cuml
clxcudfcudf-javacugraphcumlcusignalcuspatialcuxfilterlibcudflibcugraphlibcumllibrmmrmm
nightly (0.18)
nightly (0.18)stable (0.17)legacy (0.16)

Contents:

  • cuML API Reference
  • Intro and key concepts for cuML
  • cuML blogs and other references
    • Integrations, applications, and general concepts
    • Tree and forest models
    • Other popular models
    • Academic Papers
  • Training and Evaluating Machine Learning Models in cuML
  • Pickling cuML Models for Persistence
cuml
  • »
  • cuML blogs and other references
  • View page source

cuML 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

  • RAPIDS on AWS Sagemaker

Tree and forest models¶

  • Accelerating Random Forests up to 45x using cuML

  • RAPIDS Forest Inference Library: Prediction at 100 million rows per second

  • Sparse Forests with FIL

Other popular models¶

  • Accelerating TSNE with GPUs: From hours to seconds

  • Combining Speed and Scale to Accelerate K-Means in RAPIDS cuML

  • Accelerating k-nearest neighbors 600x using RAPIDS cuML

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)

Next Previous

© Copyright 2020, nvidia.

Built with Sphinx using a theme provided by Read the Docs.