Ans: Deep learning can be both supervised and unsupervised, depending on the specific architecture and task:

• Supervised deep learning uses labeled data to train neural networks for tasks like image classification or speech recognition.
• Unsupervised deep learning, such as autoencoders or generative adversarial networks, learns from unlabeled data to discover patterns or generate new samples.