End-to-end deep learning
End-to-end deep learning is the simplification of a processing or learning systems into one neural network.
Example - Speech recognition model
The traditional way - small data set
The hybrid way - medium data set
The End-to-End deep learning way – large data set
End-to-end deep learning cannot be used for every problem since it needs a lot of labeled data.
It is used mainly in audio transcripts, image captures, image synthesis, machine translation, steering in self-driving cars, etc.
Whether to use end-to-end deep learning
Before applying end-to-end deep learning, you need to ask yourself the following question: Do you have enough data to learn a function of the complexity needed to map x and y?
Pro:
Let the data speak - By having a pure machine learning approach, the neural network will learn from x to y. It will be able to find which statistics are in the data, rather than being forced to reflect human preconceptions.
Less hand-designing of components needed - It simplifies the design work flow.
Cons:
Large amount of labeled data - It cannot be used for every problem as it needs a lot of labeled data.
Excludes potentially useful hand-designed component - Data and any hand-design’s components or features are the 2 main sources of knowledge for a learning algorithm. If the data set is small than a hand-design system is a way to give manual knowledge into the algorithm.