This package is in its early phase of development. SOM performance metrics have all been tested pretty well, but they may still contain bugs. Please report them in an issue if you find some. If you ...

So, you’re looking to get better at coding with Python, and maybe you’ve heard about LeetCode. It’s a pretty popular place to practice coding problems, especially if you’re aiming for tech jobs.

GPU-accelerated Self-Organizing Maps in PyTorch with a scikit-learn API, rich visualization, and clustering --- from dimensionality reduction to Just-In-Time Learning.

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and ...

We combine a machine learning method and ensemble climate predictions to investigate windows of opportunity for seasonal predictability of European summer climate associated with the North Atlantic ...

Self-Organising Maps (SOM) are a type of unsupervised neural network that create a low-dimensional representation of input data. Neural networks, including SOMs, excel at modelling complex, nonlinear ...

Transition path sampling techniques allow molecular dynamics simulations of complex systems to focus on rare dynamical events, providing insight into mechanisms and the ability to calculate rates ...