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Stage 8: Training Dynamics & Debugging

When things go wrong—and how to fix them

Overview

Most ML education shows the happy path. This stage teaches you what to do when things go wrong—which is most of the time.

"Debugging neural networks is 80% of the job. This stage teaches that 80%."

We'll develop systematic tools for:

  1. Diagnosing problems from training curves
  2. Understanding gradients and what they reveal
  3. Finding optimal learning rates systematically
  4. Monitoring activations to detect dead neurons
  5. Debugging strategies that actually work

Why This Matters

A training run that doesn't work tells you almost nothing by default. Without proper diagnostics:

Loss: 2.34 → 2.31 → 2.29 → 2.28 → 2.28 → 2.28 → ...

Is this good? Bad? Should you wait longer? Change hyperparameters? There's no way to know.

With proper diagnostics:

Loss plateaued at step 500
Gradient norm: 1e-8 (vanishing!)
Recommendation: Add residual connections or use different activation

Now you know exactly what's wrong and how to fix it.

Common Training Failures

Symptom Likely Cause Solution
Loss → NaN Gradient explosion Reduce LR, add clipping
Loss constant Vanishing gradients Residual connections, better init
Val loss increases Overfitting Regularization, more data
Loss oscillates LR too high Reduce learning rate
Loss very slow LR too low Increase learning rate

Learning Objectives

By the end of this stage, you will:

  1. Read loss curves like a diagnostic report
  2. Implement gradient health monitoring
  3. Use the LR range test to find optimal learning rates
  4. Detect dead neurons and saturation
  5. Apply systematic debugging strategies

Sections

  1. Why Training Fails - Understanding failure modes
  2. Loss Curve Analysis - Reading the signals
  3. Gradient Statistics - Health indicators
  4. Learning Rate Finding - The LR range test
  5. Activation Monitoring - Dead neurons and saturation
  6. Debugging Strategies - Systematic approaches
  7. Implementation - Building diagnostic tools

Prerequisites

  • Understanding of gradient descent (Stage 4)
  • Familiarity with neural network training (Stage 3)
  • Experience with at least one failed training run (helpful but not required)

Key Insight

Training failures are not random—they have specific signatures. Learning to read these signatures transforms debugging from guesswork into engineering.

Code & Resources

Resource Description
code/stage-08/diagnostics.py Training diagnostics tools
code/stage-08/tests/ Test suite
Exercises Practice problems
Common Mistakes Debugging guide