Virtual Labs

a: Linearly separable problem b: Non-linearly separable problem c: Binary classification problem d: Regression problem

a: It lacks activation functions b: It lacks multiple neurons c: It cannot learn non-linear decision boundaries d: It does not have bias terms

a: Sigmoid b: ReLU c: Tanh d: Softmax

a: Increase computational efficiency b: Extract and learn features from the input data c: Reduce the number of parameters d: Speed up the training process

a: To determine the number of hidden layers. b: To control the size of weight updates during backpropagation. c: To define the output of the neurons. d: To decide the number of epochs during training.

To understand and implement a Multi-Layer Perceptron (MLP) with focus on architecture, activation functions, and training