Neural Network Weight Updates

The video tutorial explains the backpropagation algorithm used to update weights in a multi-layer perceptron network. It covers the network setup, initial weights, forward pass, error calculation, backward pass, and weight updates. The process is iterative, and the video demonstrates two iterations to show how weights are adjusted to minimize error. The tutorial concludes with a summary of the backpropagation process and its application in neural networks.

What is the initial value of the weight w13 in the given network?

• 0.8

• 0.4

• 0.6

• 0.1

Which activation function is used in the neurons of the network?

• Linear

• Tanh

• Sigmoid

• ReLU

What is the calculated output at the output neuron after the first forward pass?

• 0.5

• 0.7

• 0.68

• 0.69

What is the error at the output neuron after the first forward pass?

• 0.5

• -0.5

• 0.19

• -0.19

Which equation is used to update the weights in the network?

• Perceptron Rule

• Delta Rule

• Hebbian Learning

• Gradient Descent

What is the value of delta 5, the error term at the output unit?

• -0.0406

• 0.0406

• -0.00265

• 0.00265

After updating, what is the new value of weight w45?

• 0.8731

• 0.9

• 0.4

• 0.3971

What is the modified output at the output layer after the weight update?

• 0.6637

• 0.5

• 0.69

• 0.6820

What is the main goal of the backpropagation process?

• To increase the learning rate

• To minimize the error

• To maximize the output

• To decrease the number of neurons

What should be done if the error is not acceptable after several iterations?

• Decrease the number of layers

• Continue updating weights

• Increase the learning rate

• Stop the process