One of the coolest things about generative AI models — both large language models (LLMs) and diffusion-based image generators — is that they are "non-deterministic." That is, despite their reputation ...

A C++ implementation and Python binding of the Global complex Root and Pole Finding (GRPF) algorithm GRPF and SA-GRPF (in-progress). grpfc attempts to find all the zeros and poles of a complex valued ...

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems.

pm-remez is a modern Rust implementation of the Parks-McClellan Remez exchange algorithm. It can be used as a Rust library and as a Python package via its Python bindings. pm-remez supports the design ...

Snowpark for Python gives data scientists a nice way to do DataFrame-style programming against the Snowflake data warehouse, including the ability to set up full-blown machine learning pipelines to ...

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically ...

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion ...

Solving the sliding puzzle using a basic AI algorithm. I had published this article on Medium in September of 2018. I have decided to create more content on my LinkedIn profile, thus rewriting this ...

A new algorithm is suggested based on the central limit theorem for generating pseudo-random numbers with a specified normal or Gaussian probability density function. The suggested algorithm is very ...

When it comes to climate modelling, every computational second counts. Designed to account for air, land, sun and sea, and the complicated physics that links them, these models can run to millions of ...