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 ...

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 ...

Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

This repository contains the official implementation for the paper "Evolving Spatially Embedded Recurrent Spiking Neural Networks for Control Tasks." The code implements a framework for evolving ...

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...

Confused by neural networks? Break it down step-by-step as we walk through forward propagation using Python—perfect for beginners and curious coders alike! My Dad Was Gay — But Married To My Mom For ...

1 Department of Computer Science, PUCC, Pondicherry University, Pondicherry, India. 2 Department of Computer Science, Pondicherry University, Pondicherry, India. An ancient fossil fuel, oil is a ...

Abstract: In this paper, a robust watermarking algorithm given recurrent neural networks (RNN) is proposed. We present discrete wavelet transform (DWT) innovation for watermarking. The network is laid ...