Training deep learning models

Training a dense neural network involves various steps. First, we prepare the data. This typically involves tasks such as feature scaling, handling missing values, encoding categorical variables, and splitting the data into training and validation sets.

Then, we define the architecture of the dense neural network. This includes specifying the number of layers, the number of neurons in each layer, the activation functions to be used, and any regularization techniques, such as dropout or batch normalization.

Once the model has been defined, we need to initialize it. We create an instance of the neural network model based on the defined architecture. This involves creating an instance of the neural network class or using a predefined model architecture available in a deep learning library. We also need to define a loss function that quantifies the error between the predicted output of the model and the actual target values. The choice of loss function...