Learn GenAI
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Generative AI to Agentic AI

Master the Full Modern GenAI Stack

A structured, practical curriculum from foundational LLMs to fully autonomous Agentic systems - built by an engineer who shipped this stuff in production, not just read about it.

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133 Deep-dive notes21 Modules657 Review Q&AFree Always
01
133 deep-dive notes
Zero fluff, dual-audience (business and technical) explanations for every concept.
02
657 review questions
Interview-grade Q&A banks for every module, plus graded quizzes built from each page's checks.
03
18 hands-on code labs
PyTorch, QLoRA, vLLM, Helm, Terraform, GPU profiling, red-teaming, and agents in LangChain, LangGraph, CrewAI and Google ADK.
04
Real benchmarks
Base-vs-tuned comparisons and load tests with actual numbers, not just theory.

Your Path

1
Conceptual & Mathematical Foundations
Python & Systems, Math for ML
2
Large Language Models
Foundation Models, Evaluation & Benchmarks, Prompt & Context Engineering, PyTorch Fundamentals, Pretraining at Scale, Training LLMs from Scratch, Post-Training & Fine-Tuning
3
Fundamentals of AI Agents
Introduction to AI Agents, Agent Building Blocks, Agent Protocols: MCP & A2A, Agentic Workflows & Multi-Agent Systems
4
Building with LLMs & Agents
RAG Systems, Agent Frameworks, Production Agents
5
Production & Operations
Inference & Serving, Production Engineering
6
Industry Standards & Frontier
Cloud AI Platforms, Agent Engineering, Solutions Architecture

Curriculum

21 modules in 6 tracks, start to finish

Hover (or tap) a module to preview it - the topic list slides in on the right.

Conceptual & Mathematical Foundations
01Python & Systems
Large Language Models
03Foundation Models
Fundamentals of AI Agents
10Introduction to AI Agents
Building with LLMs & Agents
14RAG Systems
Production & Operations
17Inference & Serving
Industry Standards & Frontier
19Cloud AI Platforms
Capstones & Review
22Capstones

Learning Paths

A
App & Agent Builder
Build on top of models - Foundation Models, Evaluation and Prompt & Context Engineering, then the agent fundamentals and the Building with LLMs & Agents track.
B
Interview Prep
Accelerated review through each module's Q&A bank and its graded quiz.
C
Hands-On
Code labs from a raw PyTorch training loop to QLoRA fine-tuning, vLLM serving and multi-framework agents.
D
Full Sequence
Every module in order: math and systems foundations, then large language models, AI agents, building products with both, running them in production, and the industry frontier - the recommended path.

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100% free, always up to date, built from production experience - not a rehash of documentation.

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