Part 8/9:
Moreover, deep learning struggles significantly in generalizing beyond its training datasets. Models might excel in reproducing learned patterns but can fail dramatically when faced with unseen data, prompting questions about their real-world applicability.
Future Directions
Moving forward, researchers aim to blend deep learning's flexibility with constraints derived from domain knowledge. By imposing these constraints from areas such as physics or cognitive science, it is possible to enhance the performance of neural networks. Upcoming discussions will focus on integrating these principles into applications, ensuring that deep learning remains a powerful and reliable tool across various fields.