The Scholar project focuses on traditional machine learning techniques, introducing features like LargeVis for visualization of large-scale data, KDTree and RandomForestTree algorithms, hierarchical clustering, and new dimensionality reduction and manifold algorithms. Other projects in the Numerical Elixir organization continue to work on machine learning, with updates including structured prompting for LLMs and support for more third-party APIs. The Elixir community also offers learning resources like Livebook content and the Machine Learning in Elixir book.
Key takeaways:
- The Elixir community has made significant progress in integrating Elixir and Machine Learning, with updates including MLIR support, rich Arrow types, traditional machine learning, structured LLM, and more.
- Numerical Elixir (Nx) has been a key project in this effort, playing a similar role as Numpy within the Elixir community, with support for just-in-time compilation to both CPUs and GPUs.
- Another important project is Explorer, which provides series and dataframes for Elixir, and has now full compatibility with Arrow numeric types and improved support for streaming data in and out of S3-compatible storage.
- The Scholar project has been focusing on traditional machine learning techniques, introducing several new features such as LargeVis for visualization of large-scale and high-dimensional data, KDTree and RandomForestTree as algorithms for k-nearest neighbours classification and regression, and new dimensionality reduction and manifold algorithms.