Skip to main content
New Self-paced AI courses — learn ML, deep learning, and agents on your schedule. Enroll free

Research-driven platform

How we update as AI moves

The field does not stand still: models, libraries, and evaluation norms change. Here is how we think about refreshing lessons while keeping paths coherent for learners. For hands-on paper reading, use the Research Lab.

  1. What we tie to the literature

    Core ideas in each track connect to peer-reviewed work and established practice, not only blog summaries. The syllabus is the contract: you can read how units build before you enroll.

  2. When lessons change

    We refresh lessons when APIs, training defaults, or evaluation advice shift in ways that would mislead a learner. Large architectural trends (e.g. attention, scaling laws) are reflected in the path; hype-driven churn is not.

  3. What you see in the product

    Course and lesson pages call out practical, runnable skills first. Research Lab and field notes carry deeper reading and “why this paper” context—same account, one platform.

Research & refresh on the home page · Papers on the curriculum