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 the specific training problem