Questions
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1. Design and implement an artificial neural network to simulate XOR gate. Assign appropriate values to weights and thresholds to edges and nodes in the neural network.
Note: Do not use any DL library (Keras, pyTorch etc.) to implement the XOR gate
2. Go through the attached IRIS and MNIST datasets and design a Multi-Layer Perceptron (MLP) classifier. Train the feedforward networks using the given datasets and show the evaluation in terms of precision, recall, f-score and accuracy. Experiment with the number of neurons in the hidden layer and plot an accuracy v/s number of neurons graph.
Note: You can use any DL library to implement the classifier
Data Sets:
IRIS: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data (Divide the data into train and validation sets having 80% of each class in train and rest for the test).
MNIST: http://yann.lecun.com/exdb/mnist/
