3 Distributed primal–dual hybrid gradient algorithm This section first introduces a distributed quantile regression framework and formulates our problem as a saddle-point optimization problem.

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple.

According to Chris Olah, the scientific principles of biology, particularly in terms of evolution, are highly relevant to understanding deep learning and model interpretability. Olah draws a direct ...

The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm. In this ...

Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers.

Ruder, S. (2017) An Overview of Gradient Descent Optimization Algorithms. arXiv 1609.04747.

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper ...

A Python script with an AI algorithm that solves a 2D maze using the A* search algorithm - but, with specific movement constraints, to really force the AI earn its way out of the maze. This time, ...