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][1] 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? 


  [1]: https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf#page=34