Choosing the number of layers in a neural network depends on the complexity of the problem and the dataset. For simple tasks like linear regression, a shallow network with one or two layers may suffice. However, more complex problems like image recognition or language processing benefit from deeper architectures that can extract hierarchical features.
Experimentation and validation are key to determining the optimal number of layers. Start with a baseline model, then iteratively add layers while monitoring the performance on a validation set. Too few layers may lead to underfitting, while too many layers can cause overfitting or computational inefficiency.
Leveraging domain-specific architectures, like CNNs for image tasks or Transformers for NLP, is often effective. Predefined architectures such as ResNet or BERT provide a good starting point for many applications. Consider transfer learning when working with limited data or compute resources.