From my intuitive understanding (not an expert), very abstract description how it works in general: - you have real world problem -> task which you need to solv

From my intuitive understanding (not an expert), very abstract description how it works in general: - you have real world problem -> task which you need to solve - you build model (algorithm, math method & etc) which should solve the task - you need to find optimum of the complex function (error function) Third step is usually finding optimum of the function. Deep neural networks help you to move complexity from step 2 to step 3. One example you mentioned, when feature engineering is moved from 2 -> 3. So you can use simpler methods on step2 to solve same problems, or extend problems area which you can solve with the same complexity on step2.