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AI Curriculum for Software Engineers: Direct YouTube Links by Phase

A curriculum for engineers who already know how to code and want to go deep on AI, from the math intuition behind neural nets, to building your own autograd engine, to modern transformers, RAG, agentic workflows, and serving models in production. Every link below is direct, no channel-hopping required.

1. Mathematical Foundations & Visual Intuition

Resource	Link
3Blue1Brown - Essence of Linear Algebra (playlist)	Direct Playlist Link
3Blue1Brown - Playlists Hub	Channel Hub

2. Low-Level Neural Mechanics & Autograd Engines

Resource	Link
Andrej Karpathy - Neural Networks: Zero to Hero (playlist)	Direct Playlist Link
Andrej Karpathy - Playlists Hub	Channel Hub

3. Applied Deep Learning & Transfer Learning

Resource	Link
fast.ai - Practical Deep Learning for Coders (Jeremy Howard, Lesson 1)	Lesson 1 Walkthrough Video
fast.ai - Official Course Portal	Course Hub

4. Modern Transformer Architectures & Research Papers

Resource	Link
Umar Jamil - Attention Is All You Need (paper explained)	Direct Video Link
Umar Jamil - Coding a Transformer from Scratch in PyTorch	Direct Video Link

5. Vector Databases & Enterprise Retrieval (RAG)

Resource	Link
James Briggs - Building RAG with Databricks & Pinecone	Direct Video Link
James Briggs - LangChain Agents with Vector DBs	Direct Video Link
LlamaIndex Official - Discover LlamaIndex Series	Official Series Hub

6. Stateful Agentic Workflows & Multi-Agent Graphs

Resource	Link
LangChain Academy - Official YouTube Courses (playlist)	Direct Playlist Link
Harish Neel - LangGraph Crash Course (2025, playlist)	Direct Playlist Link
LangChain Academy - Intro to LangGraph Course	Academy Portal

7. LLM Model Serving, Low-Level Inference & MLOps

Resource	Link
DevOps & AI Toolkit - LLM Inference Jargon Decoded (vLLM, Ollama, PagedAttention)	Direct Video Link
Chip Huyen - ML Systems Design Overview	Direct Video Link

How to Use This Sheet

Go phase by phase. Each phase builds on the intuition from the one before it, so don't skip the math or the Karpathy series just because it feels slow.
Build, don't just watch. Reimplement micrograd/GPT-from-scratch style code yourself, then ship a small RAG or agent project after phases 5-6.
Bookmark this page. It'll stay updated as new high-signal resources are added to the curriculum.

Last updated: September 2026