Neural Computing And Applications

| Problem | Likely Fix | |---------|-------------| | Training loss not decreasing | Check learning rate, data normalization | | Overfitting | Add dropout, augmentation, reduce model size | | Slow convergence | Use BatchNorm, residual connections | | NaN loss | Lower LR, check for log(0), gradient clipping | | Class imbalance | Weighted loss, oversampling, Focal loss |

Several architectures have defined the landscape of modern neural applications: neural computing and applications