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 ...

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...

Abstract: The exploration of quantum advantages with Quantum Neural Networks (QNNs) is an exciting endeavor. Recurrent neural networks, the widely used framework in deep learning, suffer from the ...

This project implements and compares deep learning models (DeepAR & N-BEATS) for multivariate time series forecasting of daily temperature. Using the Darts library, it showcases feature engineering ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...

The findings of this study are potentially valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding ...

This paper seeks to forecast the daily closing prices of advanced global stock markets by employing machine learning techniques. It includes a comparative analysis of four major indices: TASI, the S&P ...