3.3 Optimization Dynamics¶

Role at Stage 3: Deep Learning¶

Tune training behavior with optimizers, schedules, initialization, regularization, and ablations. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

%%{init: {"flowchart": {"htmlLabels": true, "nodeSpacing": 80, "rankSpacing": 110}, "themeVariables": {"fontSize": "18px"}} }%%
flowchart LR
  P["<b>3.3</b><br/>Optimization Dynamics"]
  P --> S1["<b>3.3.1</b><br/>SGD Adam and Weight<br/>Decay"]
  P --> S2["<b>3.3.2</b><br/>Learning Rate<br/>Schedules"]
  P --> S3["<b>3.3.3</b><br/>Initialization and<br/>Normalization"]
  P --> S4["<b>3.3.4</b><br/>Regularization and<br/>Data Augmentation"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
3.3.1 SGD Adam and Weight Decay	SGD Adam and Weight Decay is the working skill inside Optimization Dynamics that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.3.2 Learning Rate Schedules	Learning Rate Schedules is the working skill inside Optimization Dynamics that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.3.3 Initialization and Normalization	Initialization and Normalization is the working skill inside Optimization Dynamics that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.3.4 Regularization and Data Augmentation	Regularization and Data Augmentation is the working skill inside Optimization Dynamics that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Optimization Dynamics supports a pytorch training project with loops, validation curves, checkpoints, ablations, and debugging notes..
Build and inspect this artifact: Run learning-rate and regularization ablations.
Measure progress with: Compare convergence, overfitting, stability, and final metric.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Run learning-rate and regularization ablations.

Measure: Compare convergence, overfitting, stability, and final metric.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 3: Deep Learning.