Learn Before
Build a Basic System Quickly and Iterate Using Error Analysis
When starting a new project, especially in an area in which one is not an expert, it is hard to correctly guess the most promising directions. Rather than trying to design and build the perfect system at the outset, one should build and train a basic system as quickly as possible, perhaps in a few days, and then use error analysis to identify the most promising directions and iteratively improve the algorithm.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
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Build a Basic System Quickly and Iterate Using Error Analysis
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What does error analysis primarily examine to understand an ML system's mistakes?
There is exactly one correct method for conducting error analysis on an ML system.
The process of looking at misclassified examples to understand error causes is called _____.
Match each error analysis concept to its correct description from Machine Learning Yearning.
Order the steps of conducting a basic error analysis on a dev set as described in Machine Learning Yearning.
What is the primary goal of reviewing misclassified examples during error analysis, even in categories you cannot yet fix?
Machine Learning Yearning describes error analysis as an iterative process.
Error analysis can often help you figure out how _____ different improvement directions are.
Match each error analysis activity to the benefit it provides according to Machine Learning Yearning.
Order the reasoning steps for deciding which error categories to pursue after completing an initial error analysis.
Learn After
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What is the recommended first step when starting a new ML project in an unfamiliar area?
True or False: You should spend significant time designing the perfect ML system before building or training anything.
After building a basic ML system quickly, you should use _____ to identify the most promising directions for iterative improvement.
Why does Machine Learning Yearning recommend building a basic system quickly rather than designing a perfect system at the outset?
According to Machine Learning Yearning, error analysis should be performed before building any initial system in order to avoid wasted effort.
Machine Learning Yearning recommends building and training a basic system as quickly as possible—perhaps in just a _____ days.
Match each phase of the quick-iteration workflow to its purpose as described in Machine Learning Yearning.
Order the steps of Machine Learning Yearning's recommended workflow for starting a new ML project from beginning through iteration.
What key value does Machine Learning Yearning say you gain by examining a basic system, even when it is far from the best system you could build?
Machine Learning Yearning states that experienced ML practitioners can reliably identify the best project direction before building any system.
After building a basic system, Machine Learning Yearning recommends using _____ to identify the most promising directions and iteratively improve the algorithm.
Match each Machine Learning Yearning recommendation to the reasoning the book provides for it.
Order the reasoning steps that justify the quick-build-and-iterate strategy recommended in Machine Learning Yearning.
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