An evnet driven model that uses financial time series data with New York Times information to form a LSTM recurrent neural network. There are 3 models. The first 2 models are based on price and volume ...

Abstract: In the era of artificial intelligence, the complexity and diversity of data have posed unprecedented challenges for prediction tasks. Fuzzy information granules (FIGs) have emerged as a ...

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 ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from ...

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China ...

Abstract: Communication signal prediction holds great significance for the optimization and deployment of B5G networks. In this letter, we propose a neural network model with Propagation Path ...

This study explores the use of neural networks for occupational disease risk prediction based on worker and workplace characteristics. The goal is to develop a tool to assist occupational physicians ...

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...

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 ...