Python for Deep Learning - Build Neural Networks in Python - Adding the Input Layer and the First Hidden Layer

The video tutorial explains how to use the 'add' method in a classifier to build a neural network model. It covers the dense layer parameters, including units, kernel initializer, activation function, and input dimension. The tutorial details how these parameters affect the network's structure and initialization, emphasizing the flexibility in choosing the number of neurons and the importance of weight initialization. The video also explains the role of the activation function and the significance of input dimensions based on independent variables.

What method is called to add a component to the classifier?

What does the 'units' parameter represent in the dense class?

Explain the significance of the 'kernel_initializer' parameter.

What activation function is used in the neural network described?

Why is the input dimension set to 11 in the model?