It's a deep learning procedure in which a model is fed raw data and the entire data is trained at the same time to create the desired result with no intermediate steps. It is a deep learning method in which all of the different steps are trained simultaneously rather than sequentially. End-to-end learning has the advantage of eliminating the requirement for implicit feature engineering, which usually results in lower bias. Driverless automobiles are an excellent example that you may use in your end-to-end learning content. They are guided by human input and are programmed to learn and interpret information automatically using a CNN to fulfill tasks. Another good example is the generation of a written transcript (output) from a recorded audio clip (input). The model here skips all of the steps in the middle, focusing instead on the fact that it can manage the entire sequence of steps and tasks.