Deep Learning with Python (Video 9)

The video tutorial explains how to define and implement dense layers in Keras, focusing on the methods and parameters required for layer definition. It covers the initialization of trainable weights, the role of the build method, and the importance of output shape. The tutorial also discusses the use of activation functions and regularization parameters, providing a detailed walkthrough of the dense layer implementation in Keras, with references to the Keras GitHub repository.

What is the primary purpose of the 'build' method in a Keras dense layer?

• To calculate the cost function

• To initialize and allocate memory for trainable weights

• To compile the model

• To define the activation function

Which method in a dense layer is responsible for transforming input using parameters to generate an output?

• compile

• get_output

• initialize

• train

What type of variable is the output of a dense layer in Keras?

• A boolean

• A string

• A symbolic variable

• A numerical value

What must be defined when initializing a dense layer in Keras?

• The number of epochs

• The batch size

• The output dimension

• The learning rate

In the context of Keras dense layers, what is the role of 'trainable weights'?

• They determine the model's learning rate

• They define the model's architecture

• They are variables adapted during training

• They store the model's input data