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Neural networks are commonly used in end-to-end learning systems. The term “end-to-end”
refers to the fact that we are asking the learning algorithm to go directly from the input to
the desired output. I.e., the learning algorithm directly connects the “input end” of the
system to the “output end.”
In problems where data is abundant, end-to-end systems have been remarkably successful.
But they are not always a good choice. The next few chapters will give more examples of
end-to-end systems as well as give advice on when you should and should not use them.
Page 92 Machine Learning Yearning-Draft Andrew Ng
