Neural networks can fail to converge for several reasons, including poor initialization, a high learning rate, or an insufficient model. If the weights are initialized improperly, the network may struggle to learn the right patterns from the data. A high learning rate can cause the model to overshoot the optimal solution, leading to oscillations in the loss function rather than convergence.
Additionally, inadequate data or a poorly chosen model architecture can prevent convergence. For example, a network with too few layers may be too simple to capture complex patterns, while a network with too many layers may overfit or suffer from the vanishing gradient problem.
Techniques like gradient clipping, careful weight initialization, and adaptive optimizers like Adam can help mitigate these issues and promote convergence. Regularization methods like dropout can also help prevent overfitting and improve the model’s ability to generalize.