Courses.
Lessons arranged in a useful order.
01All courses16 sequences
01LLM ConceptsFrom transformer architecture to cutting-edge research — each concept explained with intuition, math, and connections to the bigger picture.Outline02AI Agent ConceptsFoundations of autonomous AI agents — reasoning, planning, memory, tool use, multi-agent systems, and safety.Outline03AI Agent EvaluationBenchmarks, automated evaluation methods, trajectory analysis, and production monitoring for AI agents.Outline04Agentic Design PatternsArchitecture selection, tool design, error resilience, multi-agent coordination, and production patterns for agentic systems.Outline05Computer Vision ConceptsImage fundamentals through CNNs, object detection, segmentation, generative models, vision transformers, and 3D vision.Outline06LangGraph AgentsBuild production AI agents with LangGraph — tools, memory, human-in-the-loop, streaming, multi-agent systems, and deployment.Outline07LLM EvolutionThe history and trajectory of large language models — from pre-transformer foundations through the 2025 frontier.Outline08Machine Learning FoundationsMathematical foundations, learning theory, supervised and unsupervised methods, neural networks, and production ML systems.Outline09Building MCP Servers with SupabaseA hands-on guide to building Model Context Protocol servers with Supabase — from architecture to production deployment.Outline10Natural Language ProcessingText preprocessing, representation, sequence models, NLP tasks, information extraction, and multilingual NLP.Outline11Prompt EngineeringCore prompting techniques, reasoning elicitation, system prompts, structured output, context engineering, and production safety.Outline12Reinforcement LearningFoundations through deep RL, policy gradients, model-based methods, RL for language models, and landmark applications.Outline13Building a Multi-Skill AI AgentHands-on guide to building an AI agent with multiple skills — architecture, tool design, orchestration, error handling, and a capstone research agent project.Outline14Agent Harnesses & OrchestrationThe harness layer above LLMs — Claude Agent SDK, Codex CLI, Cursor, ruflo, LangGraph, AutoGen, CrewAI, and OpenAI Agents SDK compared concept-by-concept. Topologies, consensus, federation, planning, and the orchestration plumbing that turns models into systems.Outline15Advanced LLM ConceptsA second-volume tour of the techniques pushing large language models forward — advanced training, modern inference and serving, retrieval and embeddings, alignment, and adversarial robustness.Outline16Data Engineering for AI Agents on GCPThe pattern that makes agents trustworthy: ingest external data into a Cloud Storage lake, refine it through BigQuery, and serve it to agents via structured and semantic retrieval. End-to-end on Google Cloud, from raw bytes to agent context — with a curated 2024–2026 research reading list.Outline