Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

If there is one skill that sits at the heart of most successful AI and machine learning projects today, it is Python. Whether a company wants to build smarter automation, design predictive solutions, ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

Abstract: Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasingly used ...

Dave C. Swalm School of Chemical Engineering and Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, Mississippi 39762, United States Department of Chemical and ...

Linux has long been the backbone of artificial intelligence, machine learning, and data science. Its open-source foundation, flexibility, and strong developer community make it the preferred operating ...

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model versioning, data pipelines, and CI/CD integration — this guide will help ...

Experiment tracking is an essential part of modern machine learning workflows. Whether you’re tweaking hyperparameters, monitoring training metrics, or collaborating with colleagues, it’s crucial to ...