This Homework contains Basic implementation of Neural Network on MNIST dataset. The main goal is to understand how a neural network works and how to model a neural network

• utils.py -- It contains the utility functions. It has Function named train() which will calculate loss function and backpropagation method. and function test() will test_loss, correct prediction gives the accuracy. finally function plot() will calculate the Training Loss, Training accuracy, Test Loss, Test Accuarcy from functions train() and test().

• model.py -- having Class Net is basically a model/convolve of neural network architecture with convolutions and fully connected layers and configurations of these layers. The model takes input, applies convolutional operations, ReLU activations, max-pooling, and fully connected layers to output predictions.

• S5.ipynb -- It contains and uses utils.py and model.py functions. It also contains downloading MNIST dataset and loading it to my env. Then I started training the MNIST with my Neural Network Architecture. After that I evaluate the performance and plotted a graph for train, test -- loss and accuracies.