End to End learning in the context of AI and ML is a technique where the model learns all the steps between the initial input phase and the final output result. This is a deep learning process where all of the different parts are simultaneously trained instead of sequentially.
A good example of an end-to-end solution is the creation of a written transcript (output) from a recorded audio clip (input). Here, the model bypasses all the steps that occur in the middle and the emphasis is placed on the fact that it can handle the complete sequence of steps and tasks.
Tip:
clickworker provides you with high-quality input data, such as audio datasets, to train your AI system optimally.
Another example of end-to-end learning in the context of ML is self-driving cars. Their systems are trained to automatically learn and process information using a convolutional neural network (CNN). In this case, systems use previously provided human input as guidance to complete tasks.
