Neural Network Concepts Assessment

The video tutorial explains the concept of a multi-layer perceptron (MLP) network, detailing its structure with input, hidden, and output layers. It covers the process of training the network by updating weights and biases, calculating net inputs and outputs, and determining errors. The tutorial demonstrates backpropagation to reduce errors and improve network performance, concluding with an analysis of error reduction after weight updates.

What was discussed in the previous video related to multi-layer perceptron?

• Single-layer perceptron learning

• Multi-layer perceptron learning

• Convolutional neural networks

• Recurrent neural networks

How many neurons are there in the input layer of the network?

• Five

• Four

• Three

• Two

What is the learning rate given in the network?

• 1.0

• 0.1

• 0.5

• 0.8

What is the output of the input layer neurons?

• 1 0 1 0

• 1 1 0 1

• 0 0 0 0

• 0 1 1 0

What is the net input at the hidden layer neuron X5?

• 1.0

• 0.4

• 1.2

• 0.8

What is the calculated output at the output layer neuron X7?

• 0.891

• 0.769

• 0.599

• 0.419

What is the error at the output layer after the first iteration?

• 0.891

• 0.581

• 0.419

• 0.769

Which equation is used to calculate the error at the output layer neuron?

• O_k * (1 - O_k) * (Y_N - O_k)

• O_j * (1 - O_j) * Σ_k

• W_jk * Error_k

• Alpha * Error * Output

What is the error term at the output layer neuron X7?

• 1.141

• 0.526

• 0.055

• 0.101

What is the updated weight for W15 after the first iteration?

• 3.3

• 3.2

• 3.1

• 3.0