What Challenges Exist in Integrating Neural Networks with Nanotechnology?
While the potential is immense, there are several challenges to consider:
Data Quality: High-quality, reliable data is crucial for training neural networks. Inaccurate or incomplete data can lead to poor model performance. Computational Resources: Training neural networks, especially deep learning models, requires significant computational power and resources. Interpretability: Neural networks are often seen as "black boxes," making it difficult to understand how they arrive at specific decisions. Integration: Seamlessly integrating neural networks with existing nanotechnology processes and systems can be complex and challenging.
