https://github.com/82luli02/ccai321_artificial_neural_network

This repository is dedicated to the lab work completed for the CCAI 321 course. It demonstrates practical work in artificial neural networks, including the implementation of activation functions, Hamming networks, perceptron and Hebb learning rules, and two-layer networks in Python. Networks were trained and tested on both examples and real data.
https://github.com/82luli02/ccai321_artificial_neural_network

ai ann artificial-intelligence artificial-neural-networks hamming-network hebbian-learning-rule perceptron perceptron-neural-networks testing training two-layer-neural-network

Last synced: 11 months ago
JSON representation

This repository is dedicated to the lab work completed for the CCAI 321 course. It demonstrates practical work in artificial neural networks, including the implementation of activation functions, Hamming networks, perceptron and Hebb learning rules, and two-layer networks in Python. Networks were trained and tested on both examples and real data.

• Host: GitHub
• URL: https://github.com/82luli02/ccai321_artificial_neural_network
• Owner: 82Luli02
• Created: 2024-09-09T23:55:56.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-02T12:02:04.000Z (almost 2 years ago)
• Last Synced: 2025-01-17T17:19:06.927Z (over 1 year ago)
• Topics: ai, ann, artificial-intelligence, artificial-neural-networks, hamming-network, hebbian-learning-rule, perceptron, perceptron-neural-networks, testing, training, two-layer-neural-network
• Language: Jupyter Notebook
• Homepage:
• Size: 1.76 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
## Overview

This repository contains all the labs I completed during the CCAI 321 Course on Artificial Neural Network. The course consisted of 8 labs focused on building, training, and testing neural networks, exploring various architectures, learning rules, and activation functions.

## Description

- ### Lab 1 

  Introduction to Transfer Functions using Python

- ### Lab 2 

  Building a multiple input Neuron using Python

- ### Lab 3

  Building a Hamming Network using Python

- ### Lab 4

  Implementing Perceptron Learning Rule using Python

- ### Lab 5

  Implementing Supervised Hebb Rule using Python

- ### Lab 6

  Implementing Multilayer Networks using Python 

- ### Lab 7

  Implementing the Backpropagation Algorithm using Python

- ### Lab 8

  Neural Networks using sickit-learn Python

## Tools

Python: Used for implementing neural networks and various learning algorithms. 

scikit-learn: Utilized for training and testing the networks on both toy and real datasets. 

Kaggle: Used as a platform for testing and experimenting with code in an interactive environment. 

## Date Created

Winter 2023