Deep Learning - Deep Neural Network for Beginners Using Python - Feed Forward for DEEP Net

The video tutorial explains the criteria for a neural network to be considered deep, focusing on the number of hidden layers. It details the matrix dimensions for weights in different layers and describes the feedforward process to compute the output, Y hat, using sigmoid functions. Finally, it introduces the concept of error minimization in neural networks.

What is the minimum number of hidden layers required for a neural network to be considered 'deep'?

• Three

• Four

• One

• Two

In the given neural network, how many hidden layers are present?

• One

• Four

• Two

• Three

What does W1 represent in the context of the neural network?

• Weights from the first hidden layer to the second

• Weights from inputs X1 and X2 and bias to the first hidden layer

• Weights from the second hidden layer to the output

• Weights from the output to the input

What is the dimension of the weight matrix W3?

• 3x2

• 1x3

• 2x3

• 3x1

What function is applied to the product of weights and inputs during the feedforward process?

• Sigmoid

• ReLU

• Tanh

• Softmax

What is the purpose of iterating over the superscript in the feedforward process?

• To increase the number of inputs

• To change the activation function

• To adjust the learning rate

• To iterate through the layers

After calculating the output in a neural network, what is the next step?

• Add more features

• Analyze the output and minimize the error

• Increase the number of layers

• Change the activation function