{"id":20691322,"url":"https://github.com/82luli02/ccai321_artificial_neural_network","last_synced_at":"2025-08-20T12:14:01.825Z","repository":{"id":256302031,"uuid":"854887876","full_name":"82Luli02/CCAI321_Artificial_Neural_Network","owner":"82Luli02","description":"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. 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The course consisted of 8 labs focused on building, training, and testing neural networks, exploring various architectures, learning rules, and activation functions.\n\n## Description\n- ### Lab 1 \n  Introduction to Transfer Functions using Python\n\n- ### Lab 2 \n  Building a multiple input Neuron using Python\n\n- ### Lab 3\n  Building a Hamming Network using Python\n\n- ### Lab 4\n  Implementing Perceptron Learning Rule using Python\n\n- ### Lab 5\n  Implementing Supervised Hebb Rule using Python\n\n- ### Lab 6\n  Implementing Multilayer Networks using Python \n\n- ### Lab 7\n  Implementing the Backpropagation Algorithm using Python\n\n- ### Lab 8\n  Neural Networks using sickit-learn Python\n\n## Tools\nPython: Used for implementing neural networks and various learning algorithms. \u003c/br\u003e\nscikit-learn: Utilized for training and testing the networks on both toy and real datasets. \u003c/br\u003e\nKaggle: Used as a platform for testing and experimenting with code in an interactive environment. \u003c/br\u003e\n\n## Date Created\nWinter 2023\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F82luli02%2Fccai321_artificial_neural_network","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F82luli02%2Fccai321_artificial_neural_network","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F82luli02%2Fccai321_artificial_neural_network/lists"}