1Cademy - Advantages of End-to-End Deep Learning

Concept

Advantages of End-to-End Deep Learning

Lets the data speak: For a large enough data set, the neural network can figure out the appropriate function mapping from X (input) to Y (output). Moreover, by relying on a pure machine learning approach, the data and its underlying statistics are the main factors for classifying/predicting the output, and it does not have to reflect on human preconceptions.
Less hand-designing of components: This simplifies the design workflow and saves a lot of time designing individual features of the pipeline. This may contribute to a more ubiquitous adoption of this learning method.