MACHINE LEARNING

Detailed explanation-1: -Fully connected layers are defined using the Dense class.

Detailed explanation-2: -The dense layer is the fully connected, feedforward layer of a neural network. It computes the weighted sum of the inputs, adds a bias, and passes the output through an activation function. We are using the ReLU activation function for this example. This function does not change any value greater than 0.

Detailed explanation-3: -What is tensorflow dense? The dense layer in neural networks is the one that executes matrix-vector multiplication. The matrix parameters are retrieved by updating and training using the backpropagation methodology. The final result of the dense layer is the vector of n dimensions.