A.7 Continuous Learning Methodology in AI

Continuous learning is not daily news chasing. It is a loop: fundamentals, projects, frontier signals, review.

Three learning layers

Layer	What it protects	Typical output
Foundations	Skills that do not expire quickly	Python, data, math, debugging, ML basics
Projects	Ability to turn knowledge into systems	Runnable demos, reports, evaluation logs
Frontier tracking	Awareness of where the field is moving	Short notes, selected papers, small experiments

Do not let frontier tracking replace foundations and projects.

Weekly rhythm

Period	Focus	Output
Daily or every session	Course/project progress	Code, notes, error log
Weekly	Review	What changed, what is still stuck
Every 2 weeks	Small closed loop	Runnable experiment or project slice
Monthly	Consolidation	Knowledge map and next plan

Read papers lightly first

1. Title and abstract: what problem is it solving?
2. Figures and tables: what changed?
3. Method overview: what is the workflow?
4. Details: only after you know why the paper matters.

Use this note template:

Paper title:
Task:
Core change:
Most useful figure or experiment:
What I can use now:
What I still do not understand:

Turn “I saw it” into “I can use it”

1. Learn one concept.
2. Run the smallest example.
3. Change one input or parameter.
4. Put it into a project module.
5. Write one sentence in your own words.

If it never enters a project, it usually disappears from memory.

Review signals

Review when:

• You can run code but cannot explain it.
• You can copy examples but cannot modify them.
• You remember terms but cannot connect them to tasks.

When reviewing, do not reread everything. Redraw the workflow, rerun the smallest example, and list the common mistakes.