Python for Deep Learning - Build Neural Networks in Python - Adding the Next Hidden Layer

The video tutorial explains how to add a dense layer to a neural network, specifying 6 units and using uniform kernel initialization. It highlights the use of the rectifier activation function and describes the network structure, including input, hidden, and output layers.

What is the purpose of specifying 'units' when adding a dense layer?

• To determine the number of input features

• To initialize the weights of the layer

• To set the number of neurons in the layer

• To define the type of activation function

Which kernel initializer is used in the dense layer described?

• Glorot

• Uniform

• Normal

• He

Why is the input dimension parameter not needed for the new layer?

• The network does not use input dimensions

• It is only needed for the first layer

• It is automatically inferred from the previous layer

• It is not required for output layers

How many neurons are in the new layer added to the network?

• 7

• 6

• 5

• 4

What is the next step after adding input and hidden layers to the network?

• Adding more hidden layers

• Adding the output layer

• Optimizing the network

• Training the network