This study presents valuable computational findings on the neural basis of learning new motor memories without interfering with previously learned behaviours using recurrent neural networks. The ...

Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back ...

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch.

This study presents valuable computational findings on the neural basis of learning new motor memories and the savings using recurrent neural networks. The evidence supporting the claims of the ...

Lecture 2: crash course on programming neural networks In the second session, we move beyond Python basics to implement a simple neural network entirely from scratch using Python and a few standard ...

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 ...

Introduction This repository contains a basic implementation of a neural network, aiming to provide an understanding of how neural networks function at a fundamental level. It covers the core concepts ...

Neural networks are a type of artificial intelligence (AI) that can learn from data and perform complex tasks, such as image recognition, natural language processing, and speech synthesis. In this ...

In addition, since spiking neural networks require multiple iterations to obtain optimal Bayesian inference results, they are more complicated to implement. Therefore, STM employs the tree structure ...

In artificial intelligence, efficiency, and environmental impact have become paramount concerns. Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library ...