Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

This could lead to more advanced LLMs, which rely heavily on matrix multiplication to function. According to DeepMind, these feats are just the tip of the iceberg for AlphaEvolve.

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

I have investigated the symptoms of this in some detail but have not tried to find the cause: In short it seems like matrix multiplications with largeish numbers fails inconsistently in windows, and ...

The Holoplot X1 Matrix Array Sound System is a unique modular system that uses advanced beam-forming and other technology to deliver "previously inaccessible” audio performance to a variety of venues.

Abstract Can linear systems be solved faster than matrix multiplication? While there has been remarkable progress for the special cases of graph structured linear systems, in the general setting, the ...

MatMul-free LM removes matrix multiplications from language model architectures to make them faster and much more memory-efficient.

Abstract: We propose a high-density vertical AND-type (V-AND) flash thin-film transistor (TFT) array enabling accurate vector-matrix multiplication (VMM) operations. Compared to the planar AND-type (P ...

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...