Using Scholar for Traditional Machine Learning in Elixir

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Using Scholar for Traditional Machine Learning in Elixir
Sean Moriarity highlights the importance of traditional machine learning methods such as linear regression, k-nearest neighbors, and logistic regression in certain scenarios where deep learning might be overkill or unsuitable. The article introduces Scholar, a scikit-learn-like library for Elixir that's built on top of Nx and suggests it as a good starting point for those looking to implement machine learning in Elixir. With Scholar, users can leverage CPU and GPU JIT-compiled numerical definitions. Although still in pre-release, Scholar already provides a variety of algorithms and utilities, showing much promise for the Elixir ecosystem. Moriarity provides code examples for linear regression and model predictions, then extends this to logistic regression and k-nearest neighbors, all within the Elixir programming context. The piece concludes with an invitation to contribute to the still-evolving Scholar library and an acknowledgment of Mateusz Sluszniak's significant contributions.

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