Researchers at Tohoku University used machine learning potential to create large-scale models of tin (Sn) catalysts under ...

Image processing has emerged as a crucial technology in agriculture, facilitating tasks such as crop monitoring, disease detection, and yield estimation. Python, with its extensive libraries and tools ...

The new capabilities are designed to enable enterprises in regulated industries to securely build and refine machine learning ...

It's a light-weight pandas -based machine learning framework pluggable with existing python machine learning and statistics tools (scikit-learn, rpy2, etc.). Ramp provides a simple, declarative syntax ...

Super useful for keeping things organized. Let’s get into it. Harnessing Pre-installed Python Libraries in Google Colab Google Colab is ready to roll with a ton of pre-installed Python libraries, ...

Designing, building, and applying new technologies—especially those that include artificial intelligence—can be a double-edged sword: powerful and enabling on the one hand, but potentially biased and ...

More information: Zhenghao Yin et al, Experimental quantum-enhanced kernel-based machine learning on a photonic processor, Nature Photonics (2025). DOI: 10.1038/s41566-025-01682-5 ...

SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It ...

Stress can disrupt daily activities and harm health if prolonged or severe. Early detection of mental stress, indicated by changes in bio-signals like thermal, electrical, and acoustic signals, can ...