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Large End-to-End Neural Networks Can Avoid Representation Limits
With a large enough neural network and enough training data, an end-to-end system is not hampered by the limits of MFCC or phoneme-based representations and may approach the optimal error rate.
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Machine Learning
Deep Learning
Supervised Learning
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End-to-End Learning Needs Abundant Labeled Input-Output Data
Large End-to-End Neural Networks Can Avoid Representation Limits
Directly Learning Rich Outputs
What structure does end-to-end learning typically replace in a machine learning system?
Neural networks are commonly used in end-to-end learning systems.
The term 'end-to-end' refers to the learning algorithm going directly from the _____ to the desired output.
Match each output type to its description as an example of what end-to-end deep learning can produce.
Order the steps of an end-to-end sentiment classification system as described in Machine Learning Yearning.
Given the right labeled input-output pairs, what can end-to-end deep learning sometimes produce as output?
End-to-end deep learning is limited to producing outputs that are a single number.
End-to-end deep learning is an accelerating trend that allows directly learning _____ that are much more complex than a number.
Match each end-to-end learning concept to its definition from Machine Learning Yearning.
Order the reasoning steps that explain how end-to-end deep learning enables rich outputs beyond a single number.