Why Two ML Models See the Same Data Differently
Автор: sucodz
Загружено: 2025-11-11
Просмотров: 1802
Описание:
Why can two machine learning models trained on the exact same dataset produce completely different decision boundaries?
The answer lies in the architecture, inductive bias, and optimization paths that shape how each model interprets the same data.
Even with identical inputs, subtle differences in weight initialization, regularization, or gradient descent can lead to distinct learning trajectories and generalization patterns.
In this short, we explore how model design, loss landscapes, and feature representations influence learning outcomes and why understanding these differences is crucial for AI reliability, interpretability, and fairness.
Key concepts covered:
Decision boundaries in machine learning
Model bias and inductive bias
Optimization and initialization effects
Generalization in deep learning
Architecture-driven learning differences
Do models truly learn patterns from data or only from their own assumptions?
#MachineLearning #DeepLearning #AIResearch #MLTheory #DataScience #ModelBias #NeuralNetworks #DecisionBoundaries #AIInterpretability #AIExplained #ArtificialIntelligence #MLEducation #GradientDescent #AIEthics #Generalization
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