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 ...
Abstract: Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud ...
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 ...
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 ...
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 ...
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 ...
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 ...
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