Learn Before
Comparing Pipeline Components to Human-Level Performance
A pipeline can be debugged informally by comparing each component and the overall system to human-level performance. For path planning, the human comparison should use the same inputs as the component, such as only the earlier components outputs rather than raw camera images.
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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)
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Machine Learning
Deep Learning
Supervised Learning
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Data Science
Machine Learning Strategy
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Siamese Cat Pipeline Error Analysis by Parts
Component Error Counts Guide Pipeline Priorities
Informal Pipeline Error Attribution
Ambiguous Pipeline Error Attribution Cases
General Error Attribution Procedure for Multi-Step Pipelines
Comparing Pipeline Components to Human-Level Performance
What does error analysis by parts primarily tell you about a machine learning pipeline?
Error analysis by parts can only be performed using a rigorous formal procedure, not informally.
Error analysis by parts tells us what component(s) performance is worth the greatest _____ to improve.
Match each error analysis by parts concept to its correct description from Machine Learning Yearning.
Order the steps of the informal error analysis procedure Ng describes for a self-driving car pipeline.
Which three components make up the self-driving car pipeline Ng uses to illustrate informal error analysis by parts?
The primary goal of error analysis by parts is to help a developer decide which pipeline component to prioritize for improvement.
By carrying out error analysis by parts, you can _____ each mistake the algorithm makes to one or more pipeline components.
Match each component in Ng's self-driving car pipeline to the output it produces.
Order the reasoning steps a developer follows when applying error analysis by parts to prioritize pipeline improvements.
Learn After
Component Far from Human-Level Performance as Improvement Priority
Flawed Pipeline Despite Human-Level Components
When comparing the Plan path component to human-level performance in a self-driving pipeline, what inputs should the human evaluator receive?
ML Yearning describes comparing pipeline components to human-level performance as a rigorous, procedure-based debugging technique.
In informal pipeline debugging, you ask how _____ each component is from human-level performance.
Match each self-driving pipeline component from ML Yearning to its primary output or function.
Order the informal debugging questions ML Yearning recommends asking when evaluating a self-driving car pipeline.
Why does ML Yearning require the human evaluator for path planning to use only the detection components' outputs rather than camera images?
In ML Yearning's self-driving example, both the Detect cars and Detect pedestrians components feed their outputs directly into the Plan path component.
To compare the Plan path component fairly to a human, the human must plan the route using only the _____ of the detection components.
Match each informal debugging question to the pipeline element it targets in ML Yearning's self-driving car example.
Arrange the reasoning steps a practitioner follows when diagnosing a self-driving pipeline problem using human-level comparison.
Analyzing Pipeline Component Inputs for Human-Level Performance Comparison
Evaluating a Self-Driving Path Planning Component
Input Constraint in Human-Level Comparison for Path Planning