Improvising the performance of machine learning for applications in the field of computer science leads to create new algorithms. As these are being optimized, using the algorithms of the classical ...

Cracking the code to becoming an AI genius isn't about shortcuts—it’s a marathon of mathematical rigor, deep learning mastery ...

Furthermore, it is important to automate these decisions using machine learning (ML). In this article, we review the ML schemes that provide intelligence integrated with IoT used in healthcare systems ...

We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP)─the problem of identifying the stable crystal structures that will form from a given molecule based ...

🎯 We built a beginner-friendly federated learning (FL) library and benchmark: master FL in 2 hours—run it on your PC! Contribute your algorithms, datasets, and metrics to grow the FL community. 👏 ...

Historically, the primary machine learning technique used in the industry was Statistical Machine Translation (SMT). SMT uses advanced statistical analysis to estimate the best possible translations ...

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, ...

machine-learning-algorithms Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments ...

A new machine learning approach that draws inspiration from the way the human brain seems to model and learn about the world has proven capable of mastering a number of simple video games with ...

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 ...