It is known that machine learning algorithms expect feature engineering as an initial step. And consider the following paragraph, taken from 1.1 The deep learning revolution of the textbook named Deep learning with PyTorch by ELI STEVENS, LUCA ANTIGA, AND THOMAS VIEHMANN, regarding the role of feature engineering in deep learning

Deep learning, on the other hand, deals with finding such representations automatically, from raw data, in order to successfully perform a task. In the ones versus zeros example, filters would be refined during training by iteratively looking at pairs of examples and target labels. This is not to say that feature engineering has no place with deep learning; we often need to inject some form of prior knowledge in a learning system. However, the ability of a neural network to ingest data and extract useful representations on the basis of examples is what makes deep learning so powerful. The focus of deep learning practitioners is not so much on handcrafting those representations, but on operating on a mathematical entity so that it discovers representations from the training data autonomously. Often, these automatically created features are better than those that are handcrafted! As with many disruptive technologies, this fact has led to a change in perspective.

The paragraph clearly saying that we need to inject some form of prior knowledge into the learning system. What can be a concrete example for such prior knowledge we are used in deep learning systems?

It is known that machine learning algorithms expect feature engineering as an initial step. Now, consider the following paragraph, taken from 1.1 The deep learning revolution of the textbook named Deep learning with PyTorch by Eli Stevens, Luca Antiga, Thomas Viehmann, regarding the role of feature engineering in deep learning

Deep learning, on the other hand, deals with finding such representations automatically, from raw data, in order to successfully perform a task. In the ones versus zeros example, filters would be refined during training by iteratively looking at pairs of examples and target labels. This is not to say that feature engineering has no place with deep learning; we often need to inject some form of prior knowledge in a learning system. However, the ability of a neural network to ingest data and extract useful representations on the basis of examples is what makes deep learning so powerful. The focus of deep learning practitioners is not so much on handcrafting those representations, but on operating on a mathematical entity so that it discovers representations from the training data autonomously. Often, these automatically created features are better than those that are handcrafted! As with many disruptive technologies, this fact has led to a change in perspective.

The paragraph clearly saying that we need to inject some form of prior knowledge into the learning system. What can be a concrete example for such prior knowledge we are used in deep learning systems?