ML Training Debugger - Diagnose and Fix Training Issues
Overview
Systematic debugging workflow for ML training issues including loss divergence, overfitting, slow convergence, gradient problems, and performance optimization.
When to Use
- Training loss becomes NaN or infinite
- Severe overfitting (train >> val performance)
- Training not converging
- Gradient vanishing/exploding
- Poor validation accuracy
- Training too slow
Phase 1: Diagnose Issue (8 min)
Objective
Identify
[Description truncada. Veja o README completo no GitHub.]