Artificial intelligence is changing the way portfolios are built and managed. For a long time, investors relied mainly on ...

Abstract: Neural network reliability is susceptible to perturbations in both inputs and model weights. While formal verification of neural network robustness against input perturbations is a hot topic ...

Most people think of weight loss as a math problem: calories in versus calories out. In reality, your body sees it as a survival threat. When calorie intake falls or body fat decreases, biological ...

The current machine_learning directory in TheAlgorithms/Python lacks implementations of neural network optimizers, which are fundamental to training deep learning ...

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 ...

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs through layers, calculating activations, and preparing data for ...

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 ...

According to Chris Olah (@ch402), clarifying the concept of interference weights in AI neural networks is crucial for advancing model interpretability and robustness (source: Twitter, July 29, 2025).

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 ...