A training approach where a system learns to map inputs directly to outputs, minimizing intermediate steps or feature engineering.
More about End-to-End Learning
End-to-End Learning refers to training models where the entire process, from input to output, is learned directly from the data without manual intervention in feature extraction or intermediate representations. For instance, in a chatbot context, an end-to-end learning model might take user messages as input and produce chatbot responses as output without manually defined rules or intermediate processing.
This approach can be powerful as it allows the model to capture and learn intricate patterns in data. However, it often requires large amounts of data and can be more challenging to interpret or debug compared to models with more defined stages.
