Question - What do you understand by end-to-end learning?
Answer -
It is a deep learning process where a model gets raw data as the input and all the various parts are trained simultaneously to produce the desired outcome with no intermediate tasks. The advantage of end-to-end learning is that there is no need for implicitly doing feature engineering which usually leads to a lower bias. A good example that you can quote in the content of end-to-end learning is driverless cars. They use human-provided input as guidance and are trained to automatically learn and process the information using a CNN to complete tasks.