| name | rohitg00--ai-engineering-from-scratch |
| description | Curriculum AI engineering từ toán học lên production — 503 bài học, 20 phase, 320 giờ. MIT license. Build từ first principles: backprop, transformer, LLM, agent, swarm. |
| allowed-tools | Bash, Read, Write |
| user-invocable | true |
AI Engineering from Scratch: curriculum toàn diện dạy AI từ đầu — không phải dùng API, mà tự build từ toán học. 503 lesson → 503 artifact (skills, agents, MCP servers).
20 Phase Overview
Phase 0-3 — Foundations
0: Dev environment + tooling
1: Linear algebra, calculus, probability, optimization
2: Classical ML (regression, decision trees, clustering)
3: Deep learning, backprop, PyTorch, JAX
Phase 4-6 — Specialized Domains
4: Computer Vision (CNN, detection, segmentation, GAN, diffusion)
5: NLP (tokenization, embeddings, transformers, evaluation)
6: Speech & Audio (ASR, TTS, voice cloning)
Phase 7-12 — Advanced AI
7: Transformers deep dive (attention, BERT, GPT, scaling)
8: Generative AI (VAE, diffusion, video, 3D)
9: Reinforcement Learning (Q-learning, PPO, RLHF)
10: LLMs from scratch (tokenizer → pre-train → instruction tune → DPO → quantization)
11: LLM Engineering (prompting, RAG, fine-tune, production)
12: Multimodal AI (VLM, video, embodied agents)
Phase 13-17 — Systems & Production
13: Tools & Protocols (function calling, MCP, API design)
14: Agent Engineering (loops, memory, planning, LangGraph)
15: Autonomous Systems (long-horizon, self-improvement, safety)
16: Multi-agent (coordination, swarms, negotiation)
17: Infrastructure (serving, quantization, observability, compliance)
Phase 18-19 — Responsible Dev + Capstone
18: Ethics, safety, alignment, red-teaming
19: 17 end-to-end products + 9 deep-build tracks
Entry Points
| Background | Start | Time |
|---|
| Beginner | Phase 0 | ~306h |
| Python fluent | Phase 1 | ~270h |
| ML experienced | Phase 3 | ~200h |
| DL expert → LLMs | Phase 10 | ~100h |
| Senior → agents only | Phase 14 | ~60h |
Install Skills
npx skills add rohitg00/ai-engineering-from-scratch
python3 scripts/install_skills.py <target>
Lesson Format (mỗi bài)
1. Motto — big idea
2. Problem — what we're solving
3. Concept — theory + math
4. Build it — from raw math, no framework
5. Use it — with PyTorch/HuggingFace/etc
6. Ship it — reusable artifact
Key Topics để Reference
- Backpropagation + automatic differentiation
- Attention mechanism + transformer architecture
- Fine-tuning, RLHF, DPO, Constitutional AI
- RAG + semantic search
- Quantization + inference optimization
- Multi-agent orchestration + swarm coordination
- Production deployment + observability
Source
https://github.com/rohitg00/ai-engineering-from-scratch · MIT
