Using Java Swing to implement single layer perceptron neural network.

3 versions of Perceptron: normal Perceptron; Perceptron GUI; Multilayer Perceptron GUI, back propagation，感知机，感知器，BP 神经网络，反向传播，多层感知器，多层感知机

Learn Neural Networks using Java

Example of using a Perceptron using 1 neuron to find out if it is a living being or not, using the Java Language;

Neuron network lib

Explore the world of Artificial Intelligence with these Java projects! Implement the Perceptron, DFS for puzzles, and Genetic Algorithm from scratch without ML libraries. Solve classic problems and learn AI concepts. 🤖🔍🧬

A Binary Multi-Layer Neural Network

AI final project to classify ASCII art digits and faces. Implemented various fundamental machine learning algorithms such as K-Nearest Neighbors, Naive Bayes, and Perceptron. 5-Nearest Neighbors was more than 90% accurate on 1000 test digits and 150 test faces using 6000 digits and 752 faces as training samples respectively.

Solving tasks given by PJWSTK and own projects based on artificial intelligence.

Language recognition using Perceptrons

Demonstrate perceptron (based on nature of code)

Implementation of the Perceptron Algorithm for single neuron training. Multiple data transformations attempted with results shown at the attached link.

This project implements a Perceptron, which is a fundamental algorithm used in machine learning for binary classification tasks. The Perceptron learns from a set of training data and adjusts its weights to classify new data points.

Repository containing machine learning algorithms and datasets

Atividade de IA, usando o algoritmo Preceptron.

This contains the most commonly used machine learning and deep learning models written from scratch without the use if any libraries except Numpy which is used for calculations

Forecast COVID-19 risk in any geographic area using a predictive neural network.

My second neural network project. You can select the number of hidden layers and the number of neurons in the layers. Dynamic Step is experimental, thus you should keep it disabled. Overall the network is quite inefficient.

A supervised learning of binary classifier