Artificial Neural Networks Lab

Instructions:

1. The course objectives and course outcomes are identical to that of (Artificial Neural Networks) as this is the practical component of the corresponding theory paper.
2. The practical list shall be notified by the teacher in the first week of the class commencement under intimation to the office of the Head of Department / Institution in which the paper is being offered from the list of practicals below. 

List of Programs

1. Setting up the Spyder IDE Environment and Executing a Python Program
2. Implementation of different activation function to train ANN.
3. Implementation of different learning rules.
4. Implementation of Perceptron Networks.
5. Program to calculate output in a multi-layer feed forward network.
6. How the Perceptron Learning rule works for Linearly Separable Problem.
7. How the Perceptron Learning rule works for Non-Linearly Separable Problem.
8. Program to train a neural network to classify two clusters in a 2-dimensional space
9. Make Predictions with k-nearest neighbors on the Iris Flowers Dataset
10. Installing Keras, Tensorflow and Pytorch libraries and making use of them