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

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 ...

What if you could create your very own personal AI assistant—one that could research, analyze, and even interact with tools—all from scratch? It might sound like a task reserved for seasoned ...

A new international study has introduced Curved Neural Networks—a new type of AI memory architecture inspired by ideas from geometry. The study shows that bending the "space" in which AI "thinks" can ...

This is my journey to implement NNs from first principles, one neuron at a time. In this notebook we build a neural network with 2 neurons in layer 1, and 1 neuron in layer 2. We then visualize how it ...

Abstract: Due to their synaptic-like characteristics and memory properties, memristors are often used in neuromorphic circuits, particularly neural network circuits. However, most of the existing ...

ABSTRACT: An algorithm is being developed to conduct a computational experiment to study the dynamics of random processes in an asymmetric Markov chain with eight discrete states and continuous time.

Abstract: The brain emotional learning network offers several advantages when compared to traditional neural networks. It features a simpler structure, low computational complexity, and fast training ...

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 ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...