Скачать или смотреть Why Two ML Models See the Same Data Differently

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