When Covid-19 struck in 2020, Sashikumaar Ganeshan at the Indian Institute of Science, Bangalore built a model to predict the spread of the contagion, marking his deep immersion into AI technologies.

An efficient neural screening approach rapidly identifies circuit modules governing distinct behavioral transitions in ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Looking back at 2025, it’s obviously, on a daily basis, why the broadcast networks are dismissed by most Americans as a source of daily advertising for one side of the political debate. This tilt has ...

Abstract: This study introduces a novel strategy for waste segregation employing Convolutional Neural Networks (CNNs) and Python programming. By harnessing CNNs’ image feature extraction capabilities, ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland ...

The neural networks dominating AI in recent years have achieved a remarkable level of behavioral flexibility, in part due to their capacity to learn new tasks from only a few examples. These ...

Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential ...