unit 2 quiz 2

In a neural network, knowing the weight and bias of each neuron is the most important step. If you can somehow get the correct value of weight and bias for each neuron, you can approximate any function. What would be the best way to approach this?

• Assign random values and pray to God they are correct

• Search every possible combination of weights and biases till you get the best value

• Iteratively check that after assigning a value how far you are from the best values, and slightly change the assigned values values to make them better

• None of these

What are the steps for using a gradient descent algorithm?

Calculate error between the actual value and the predicted value

Reiterate until you find the best weights of network

Pass an input through the network and get values from output layer

Initialize random weight and bias

Go to each neurons which contributes to the error and change its respective values to reduce the error

• 1, 2, 3, 4, 5

• 5, 4, 3, 2, 1

• 3, 2, 1, 5, 4

• 4, 3, 1, 5, 2