Natural Language Processing (NLP)

Transformers

Advanced exploration of attention-based models in NLP, focusing on judgment, calibration, and restraint in ambiguous scenarios.

Goal:

Learn how attention-based models process language.

3Lessons

6Micro-lessons

AdvancedDifficulty

Lesson 1

Navigating uncertainty and trade-offs in transformer attention layers.

Start2 Micro-lessons

Micro lesson 1

Ambiguous Signal Prioritization

Micro lesson 2

Overfitting to Spurious Patterns

Lesson 2

Recognizing nonlinear failures and compounding errors as transformer models scale.

Start2 Micro-lessons

Micro lesson 1

Scaling-Induced Instability

Micro lesson 2

Silent Accumulation of Error

Lesson 3

Identifying hidden costs and knowing when to break from standard transformer strategies.

Start2 Micro-lessons

Micro lesson 1

When Regularization Backfires

Micro lesson 2

Ignoring Outliers Too Soon