This repository provides an example of a neural network implementation using the backpropagation algorithm. The network is trained on a toy dataset, and it can be used for prediction tasks.

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

Understanding how back-propagation works will enable you to use neural network tools more effectively.

Artificial neural networks are inspired by the early models of sensory processing by the brain. An artificial neural network can be created by simulating a network of model neurons in a computer.

Neural Network Backpropagation Example This project implements a simple neural network with one hidden layer using NumPy to demonstrate forward and backward propagation.

Understanding how back-propagation works will enable you to use neural network tools more effectively.