Growing Neural Networks:

• Begin with a compact or small neural network.
• Add units or neurons to the network dynamically during the training process.

Addressing Underfitting:

• The growth mechanism is triggered when the current network underfits the training data.
• Underfitting occurs when the network is too simple to capture the complexity of the data.

Stopping Criteria:

• Continue adding units until a stopping criterion is met or until overfitting is detected.

Detecting Overfitting:

• Overfitting is detected when the network's performance on the training data is significantly better than on new, unseen data.

Optimal Architecture Search:

• The goal is to find the optimal architecture for the neural network.
• Optimal architecture balances between underfitting and overfitting, achieving good generalization.

Adaptive Learning:

• The network learns to adapt its architecture based on the complexity of the data and the learning task.

Iterative Process:

• Growing neural networks involve an iterative process of adding units, training, and evaluating until an optimal architecture is achieved.

Flexible and Scalable:

• The approach makes the network flexible and scalable, adapting to different complexities within the data.

Balancing Complexity:

• The growth mechanism aims to strike a balance between simplicity and complexity, avoiding both underfitting and overfitting.