In this tutorial, we demonstrate how we simulate a privacy-preserving fraud detection system using Federated Learning without relying on heavyweight frameworks or complex infrastructure. We build a ...

Abstract: We propose an effective approach toward implementing personalized federated learning at the edge of wireless networks. The scheme employs a bi-level optimization framework to personalize the ...

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

According to DeepLearning.AI (@DeepLearningAI), the new PyTorch for Deep Learning Professional Certificate, led by Laurence Moroney, provides in-depth, practical training on building, optimizing, and ...

Abstract: Federated Learning is a distributed machine learning paradigm that enables model training across decentralized devices holding local data, thereby preserving data privacy and reducing the ...

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...

At this year’s Credit Scoring and Credit Control Conference in Edinburgh, colleagues Ben Archer and Peter Szocs presented on a topic gaining significant attention: how federated learning can support ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...