This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

When managing associate Tanya Sadoughi found a recurring problem in the banking and finance practice, she put her newfound ...

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs ...

In this work we present two main contributions: the first one is a Python implementation of the discrete approximation of the Laplace-Beltrami operator (LBO) (Belkin et al., 2008) allowing us to solve ...

Abstract: This article presents the development, implementation, and validation of a loss-optimized and circuit parameter-sensitive triple-phase-shift (TPS) modulation scheme for a dual-active-bridge ...

The current machine_learning directory in TheAlgorithms/Python lacks implementations of neural network optimizers, which are fundamental to training deep learning models effectively. To fill this gap ...

AI for Science Institute (CUAISci), Cornell University, Ithaca, New York 14853, United States Systems Engineering, College of Engineering, Cornell University, Ithaca, New York 14853, United States AI ...

Taylor Stanberry, a Naples native, won the 2025 Florida Python Challenge, removing 60 Burmese pythons. The annual competition aims to control the invasive python population in Florida. This is the ...

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...