An advanced bridge from deep-learning practice to language-model mechanics, evaluation, retrieval, tool use, and operational agent workflows.
Connect neural-network training and transfer-learning practice with language-model internals, model evaluation, prompting, agent loops, MCP tool connections, and retrieval-oriented workflows. The material spans long-form technical lessons and shorter demonstrations; use the assignments to distinguish durable concepts from version-sensitive commands and to test every system boundary.
Relate training, validation, transfer learning, tokens, and language-model behavior at a systems level. Design a small evaluation plan for prompts, model outputs, and retrieval-grounded answers. Specify an agent loop with goals, tools, memory, success criteria, and verification. Assess tool connections and workflow automation for provenance, permissions, and failure recovery.
Working Python knowledge, basic machine-learning vocabulary, comfort reading technical documentation, and familiarity with version control. This is not a substitute for a full deep-learning course.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
7 lessons · Advanced · Full course.
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