Guidelines: - Asignnsent should subnait ca Bt.MCKBOARD only - Qostions are from chapta 1 and 2. You can refer the rexvurces from the intenst. - The last date of abbrission wall be on Late saberiwion will result in reduction of marks Questions: 1. Construct the following (CLO 2.1) - Single-layered feed forward network with 2 input neurons and 4 output neurons. - Mulit-layered feed forward network with 3 input neuroes, 6 hadden neuron and 2 outputs. - A fully recurrent network with 4 neurons, but with no self-feedback. 2. A neuron ' k ' receives input from other five neurons, as 2.65,3.85,4.2 and 0.1 The corresponding synaptic weights are 1.5,0.2,1.3 and 0.5. Assume the bias as 1. (CLO 1.1) - Draw the computational model of ncuron '. - Calculate the linear output of the neuron. - Apply sigmoid function as activation function 3. What are the different types of activation function describe them with the help of an cxample and diagram. (CL.O I.1) 4. How to assign credit assignment problem with two sub problems for a ncural network's output to its intemal (free) parameters? (CLO 2.2) 5. Discuss this relationship between supervised learning and reinforcement learning (CLO 2.2) 6. Explain in detail the method of steepest descent in single layer perceptron. (CLO 2.1) 7. In a perceptron network there are two inputs and two synaptic weights. (CLO 2.1) Classify the perceptron is Class 1 of Class 2 using decision boundary. Find the given inputs (3,7) lies above line or below line in the quadratic equation 4x+ 2y15. Guidelines: - Asignnsent should subnait ca Bt.MCKBOARD only - Qostions are from chapta 1 and 2. You can refer the rexvurces from the intenst. - The last date of abbrission wall be on Late saberiwion will result in reduction of marks Questions: 1. Construct the following (CLO 2.1) - Single-layered feed forward network with 2 input neurons and 4 output neurons. - Mulit-layered feed forward network with 3 input neuroes, 6 hadden neuron and 2 outputs. - A fully recurrent network with 4 neurons, but with no self-feedback. 2. A neuron ' k ' receives input from other five neurons, as 2.65,3.85,4.2 and 0.1 The corresponding synaptic weights are 1.5,0.2,1.3 and 0.5. Assume the bias as 1. (CLO 1.1) - Draw the computational model of ncuron '. - Calculate the linear output of the neuron. - Apply sigmoid function as activation function 3. What are the different types of activation function describe them with the help of an cxample and diagram. (CL.O I.1) 4. How to assign credit assignment problem with two sub problems for a ncural network's output to its intemal (free) parameters? (CLO 2.2) 5. Discuss this relationship between supervised learning and reinforcement learning (CLO 2.2) 6. Explain in detail the method of steepest descent in single layer perceptron. (CLO 2.1) 7. In a perceptron network there are two inputs and two synaptic weights. (CLO 2.1) Classify the perceptron is Class 1 of Class 2 using decision boundary. Find the given inputs (3,7) lies above line or below line in the quadratic equation 4x+ 2y15
