What is the process of adding additional layers to a backpropagation network called?Select one:a.Optimizingb.Tuningc.Deepeningd.Regularizing

The process of adding additional layers to a backpropagation network is referred to as "Deepening." This term comes from the concept of deep learning, which involves creating neural networks with multiple hidden layers.

Explanation

1. Deep Networks: When we say a model is "deep," we mean that it has many layers of neurons. Each layer learns different levels of abstraction in the data. By increasing the number of layers, a network can capture more intricate patterns and features in the training data, leading to better performance on complex tasks.

2. Backpropagation: Backpropagation is the algorithm used for training neural networks. It calculates the gradient of the loss function with respect to each weight by the chain rule, allowing the model to adjust its weights based on the error it made in the previous prediction.

3. Other Options:

   • Optimizing refers to modifying the learning process to improve performance but does not directly involve adding layers.
   • Tuning usually involves adjusting hyperparameters rather than the structure of the network itself.
   • Regularizing is a technique to prevent overfitting by adding a penalty for larger weights or limiting model complexity, but it doesn’t refer to adding layers.

Given this understanding, the correct answer is c. Deepening.