What are some challenges in using Keras for Nanotechnology?
While Keras offers many advantages, there are several challenges in applying it to nanotechnology:
Data Quality: High-quality, labeled data is essential for training accurate models. In nanotechnology, obtaining such data can be difficult due to the complexity and variability of nanomaterials. Computational Resources: Training deep learning models can be computationally intensive, requiring access to powerful hardware such as GPUs, which might not be readily available in all research settings. Interdisciplinary Knowledge: Effective use of Keras in nanotechnology requires a combination of expertise in both fields, which can be challenging to find.
