From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

To help professionals build these capabilities, we have curated a list of the best applied AI and data science courses.

Objective: To construct a prediction model for teicoplanin (TEIC) plasma concentrations through machine learning and deep learning techniques in patients with liver disease using real-world clinical ...

However, NGD faces several challenges associated with gamma-ray generation and attenuation complexities. Unlike GGD, which utilizes 0.662 MeV monoenergetic γ rays from a 137 Cs source, NGD employs ...

Tech Soft 3D, the world leader in providing engineering software development toolkits (SDKs), officially launches HOOPS AI, the first framework purpose-built to unlock AI and machine learning for CAD ...

The workflow integrates automation, scalability, and predictive modeling to address the complexity of oligonucleotide analysis, enhancing method development and impurity profiling. It provides ...

illustrating the comprehensive zero-shot benchmark of 19 universal machine learning interatomic potentials and the dominant impact of training data composition for surface energy prediction. A ...

In this tutorial, we combine the analytical power of XGBoost with the conversational intelligence of LangChain. We build an end-to-end pipeline that can generate synthetic datasets, train an XGBoost ...

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...