Learn Before
Difficulty of Knowing the Best ML Approach in Advance
For a new machine learning problem, it is very difficult to know in advance which approach will work best. Even experienced machine learning researchers usually try out many dozens of ideas before they discover something satisfactory.
0
1
Tags
D2L
Dive into Deep Learning @ D2L
Machine Learning
Deep Learning
Supervised Learning
Data Science
Machine Learning Strategy
Related
Representational Learning
Supervised Learning
Kaggle Platform
Predictive Analytics for Accelerated Decision-Making
Development Set (Dev Set)
Test Set
Optimizing and Satisficing Metrics
Difficulty of Knowing the Best ML Approach in Advance
Iterative Loop of Machine Learning Development
Bias and Variance as Two Major Sources of Error
End-to-End Learning
Machine Learning Pipeline System
Sentiment Classification
Machine Learning Strategy
Scale Drives Machine Learning Progress
Learn After
Why must ML practitioners expect to iterate through many approaches for a new problem?
Experienced ML researchers can typically identify the best approach for a new problem before running any experiments.
For a new ML problem, it is very _____ to know in advance which approach will work best.
Match each ML development concept to its accurate description per ML Yearning.
Order the steps of the typical iterative ML problem-solving process described in ML Yearning.
According to ML Yearning, how many ideas does an experienced ML researcher typically try before finding a satisfactory solution?
The difficulty of predicting the best ML approach in advance applies only to beginners, not to experienced researchers.
Even experienced ML researchers will usually try out many _____ of ideas before discovering something satisfactory.
Match each ML problem-solving claim to its correct implication according to ML Yearning.
Order the logical reasoning steps that explain why ML development must be iterative, per ML Yearning.