Why do we randomly shuffle the training examples before using SGD optimization?
Why do we randomly shuffle the training examples before using SGD optimization?
Homework Answers
Hey here is answer to your query.
Main reason to shuffle training data before using SGD is to get unbiased result for the real gradient. Like mostly data is in ordered manner so if we provide that data to SGD then there is high probability that it will bias the gradient which will eventually lead to poor convergence . which will not be good for our model as then overall accuracy of model will be effected in negative way.
In case of any doubt please comment. Happy Learning :)
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Why do we randomly shuffle the training examples before using
SGD optimization?
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coding is needed, please use python
A data set has 600 examples. To properly test the performance of the final hypothesis, you set aside a randomly selected subset of 200 examples which are never used in the training phase; these form a test set. You use a learning model with 1,000 hypotheses and select the final hypothesis g based on the 400 training examples. We wish to estimate Eout (g). We have access to two estimates: Ein(g), the in-sample...
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