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network, because it learnt the training data too well, it may skip to understand or 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captures that as well so both of these overfitting and underfitting are not desirable in case 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machine learning algorithms so to put graphically","confidence":0.6799999999999999,"words":[{"word":"machine","start_time":124.452,"end_time":124.692},{"word":"learning","start_time":124.713,"end_time":124.973},{"word":"algorithms","start_time":125.034,"end_time":125.435},{"word":"so","start_time":127.422,"end_time":127.542},{"word":"to","start_time":127.562,"end_time":127.683},{"word":"put","start_time":127.703,"end_time":128.265},{"word":"graphically","start_time":129.409,"end_time":129.75}],"alternatives":[],"language":"en"},{"transcript":" you find that all these blue dots in these examples they are the training 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