Common challenges in training neural networks include overfitting, where the model performs well on training data but poorly on unseen data. Regularization and data augmentation mitigate this issue.
Vanishing and exploding gradients hinder training in deep networks, particularly with sigmoid or tanh activations. Techniques like ReLU activation and batch normalization address these problems.
Resource constraints, like insufficient computational power or labeled data, also pose challenges. Leveraging transfer learning, optimizing architectures, and using cloud-based solutions can help overcome these limitations.