In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

Deep learning approaches, particularly convolutional neural networks (CNNs) and other architectures, were used in 49 papers. These models excel at image-based tasks such as land cover classification, ...

If we say that the junior and intermediate stages are about "refining skills," then the advanced stage of Python is about ...

“Blender is the starting point because it’s free, open source, and used by a huge range of creators,” Fukuyama says. “But the ...

Discover how Python is evolving in 2025 with new tools, frameworks, and trends shaping AI, data science, and API development.

The Parallel-R1 framework uses reinforcement learning to teach models how to explore multiple reasoning paths at once, ...

The fundamentals of generative systems make AI-powered robots more general. They can be presented with entirely new ...

A research team presents XFruitSeg, a deep learning model designed to accurately segment three-dimensional computed tomography (CT) images of plant fruits.

Learn how Tongyi DeepResearch combines cutting-edge reasoning and open-source flexibility to transform advanced research ...

In this important work, the authors present a new transformer-based neural network designed to isolate and quantify higher-order epistasis in protein sequences. They provide solid evidence that higher ...

DeepMind's safety framework is based on so-called "critical capability levels" (CCLs). These are essentially risk assessment rubrics that aim to measure an AI model's capabilities and define the point ...