Though deep learning is a revolutionary technology but it has few drawback as well, a few of them are:

Deep learning algorithms require large amounts of labeled data in order to learn effectively, which can be a challenge sometimes

Need large amounts of labeled data

Deep learning algorithms are computationally intensive, and require specialized hardware such GPUs to run effectively.

Computational intensive

Deep learning algorithms can be difficult to interpret, as they often consist of multiple layers of artificial neural networks that are difficult to understand

Lack of interpretability

Deep learning algorithms can take a long time to train, particularly when using large amounts of data

Long training times

Deep learning algorithms can be difficult to deploy in real-world situations, as they often require specialized hardware and infrastructure.

Difficulty in deploying