What is one downside to deep learning

One notable downside to deep learning is its insatiable appetite for data. While these models thrive on vast datasets, acquiring and curating such data can be resource-intensive, often leading to challenges in accessibility and ethical considerations.

Why deep learning is difficult

Deep learning, while revolutionary, poses significant challenges. Its complexity stems from vast data requirements, intricate architectures, and the need for extensive computational power. Moreover, the opacity of neural networks often obscures understanding, complicating troubleshooting and trust.