Yes, Restricted Boltzmann Machines (RBMs) can serve as an example of deep learning. In particular, deep learning networks like deep belief networks can be formed by "stacking" RBMs, which in turn can be fine-tuned using gradient descent and backpropagation. Despite their deep learning applications, RBMs are indeed a more general machine learning concept as well. They have found applications in dimensionality reduction, classification, collaborative filtering, feature learning, topic modeling, and more. They can also be trained in either supervised or unsupervised ways, depending on the task.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence