Confused by neural networks? Break it down step-by-step as we walk through forward propagation using Python—perfect for beginners and curious coders alike!

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning!

Heterogeneous graph neural networks (HGNs) have attracted more and more attention recently due to their wide applications such as node classification, community detection, and recommendation.

In this letter, we introduce the first spiking-based network optimized for synthetic aperture radar (SAR) ship detection and compare its performance with conventional neural networks (CNNs). Spiking ...

Abstract Neural network architecture determines its functional output. However, the detailed mechanisms are not well characterized. In this study, we focused on the neural network architectures of ...

In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along ...

примеры нейронных сетей и их настроек для разработки искусственного интеллекта - TAUforPython ...

What is this book about? Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation ...