ML Coding Lecture: Validating Mathematical Theory in Practice
Negative_War_65 · reddit · 2026-08-17
This is the second lecture in a Machine Learning coding series, focusing on understanding outputs and validating mathematical principles through implementation.
Key Topics:
- Theoretical Validation: Demonstrating the equivalence of Negative Log Likelihood and Mean Squared Error under Gaussian distribution assumptions.
- Loss Functions: Exploring L1 and L2 loss curves and Gaussian output distribution for modeling uncertainty.
- Model Analysis: Using linear and polynomial regression to explain underfitting and overfitting, followed by implementing deep neural networks for automatic feature learning.
The lecture bridges the gap between theoretical concepts and practical code implementation.
Related event: ML Programming Course Validates Mathematical Theories(2 posts)→
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