Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by ...

The quality of AI-generated artifacts and answers improves when certificates are demanded, even if the evidence provided by ...

The Virtual Brain Inference (VBI) toolkit enables efficient, accurate, and scalable Bayesian inference over whole-brain network models, improving parameter estimation, uncertainty quantification, and ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

The demo runs entirely in your browser (no backend required) and shows an animated XOR training visualization.

This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

CPUs and GPUs are old news. These days, the cutting edge is all about NPUs, and hardware manufacturers are talking up NPU performance. The NPU is a computer component designed to accelerate AI tasks ...

Abstract: In this study, we present a novel approach to adversarial attacks for graph neural networks (GNNs), specifically addressing the unique challenges posed by graphical data. Unlike traditional ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...