Results for “jmx”

High-performance numerical computing with JAX, covering functional transformations, Flax NNX, and best practices for ML research.

Provides guidance on using JAX for machine learning and mathematical analysis, covering core concepts, transformations, ML specifics, control flow, and parallelism.

Implements Manifold-Constrained Hyper-Connections (mHC) using Doubly Stochastic Matrices to improve deep learning stability.