The video tutorial explains how to build a neural network with three computational layers, including an output layer. It covers the initialization of weights for each layer, the implementation of a forward step function, and the process of matrix multiplication for layer outputs and inputs. The tutorial concludes with an introduction to activation functions and their significance in neural networks.

How many computational layers, including the output layer, are in the example neural network?

• 2

• 5

• 3

• 4

What is the number of neurons in the second computational layer?

• 2

• 4

• 3

• 1

What is the dimension of the weight matrix for the first computational layer if the input dimension is 10?

• 10 by 2

• 2 by 10

• 3 by 10

• 10 by 3

In the forward step function, what is the role of the variable 'H'?

• It represents the hidden state or outputs of the computational neurons.

• It holds the final output of the network.

• It stores the input data.

• It is used to initialize weights.

What happens to the input dimensions as they pass through each layer in the network?

• They decrease with each layer.

• They change according to the number of neurons in the next layer.

• They increase with each layer.

• They remain the same.

If the last layer of the network has 5 neurons, what will be the nature of the output?

• A single number

• A 2D matrix

• A tensor or array with five numbers

• A 3D matrix

What is the next topic to be covered after the forward step function?

• Loss functions

• Activation functions

• Weight initialization

• Backpropagation