Machine_Learning_Yearning
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Machine_Learning_Yearning
Draft VersionMA CH I N E LEARNING YEARNINGTechnical Strategy for Al Engineers, In the Era of Deep Learningdeeplearning.aiMachine Learning Yearning is Machine_Learning_Yearning a deeplearning.ai project.© 2018 Andrew Ng. All Rights Reserved.Page 2Machine Learning Yeaming-DraftAndrew NgTable of Contents1Wliy Machine T eaming Strategy2How to use this book to help your team3Prerequisites and Notation4Scale drives machine learning progress5Your development and test sets6Your Machine_Learning_Yearningdev and rest sets should come from the same distribution7Iknv large do die devAest sets need IQ be?8Establish a single-number evaluation metric for yoMachine_Learning_Yearning
ur team to optimize9Optimizing and satisficing metrics10Having a dev set and metric speeds up Iterations11When 1Draft VersionMA CH I N E LEARNING YEARNINGTechnical Strategy for Al Engineers, In the Era of Deep Learningdeeplearning.aiMachine Learning Yearning is Machine_Learning_Yearningideas15Evaluating multiple ideas 111 parallel dtuing error analysis16Cleaning up mislabeled dev and test set examples17If you have a large dc\ set, split it into two subsets, only one of winch you look al18How big should the Eyeball and Blackbox dev sets be?19lakcaways: Basic error analysis20Bias an Machine_Learning_Yearningd Variance: The two big sources of error21Examples of Bias and Variance22Comparing to the optnnal error rate23Addressing Bias and Variance21 Bras vs.Machine_Learning_Yearning
Variance tradeoff25Tecluriques for reducing avoidable biasPage 3Machine Learning Yearn ing DraftDraft VersionMA CH I N E LEARNING YEARNINGTechnical Strategy for Al Engineers, In the Era of Deep Learningdeeplearning.aiMachine Learning Yearning is Draft VersionMA CH I N E LEARNING YEARNINGTechnical Strategy for Al Engineers, In the Era of Deep Learningdeeplearning.aiMachine Learning Yearning isGọi ngay
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