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Open-Source AI Engineering Curriculum From First Principles

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Low-confidence capture. Useful as a lead, not a recommendation.

Original post

TL;DR

An open-source, 416-lesson AI engineering curriculum spanning 20 phases, covering everything from linear algebra and backpropagation fundamentals through transformers, PyTorch, and autonomous agent systems. It is structured around a four-step learning loop: read the problem, derive the math, write the code, and run the test. Addresses a gap in AI education where practitioners can use high-level AI frameworks and APIs without understanding the underlying mechanics (e.g., attention, backpropagation). The curriculum builds foundational understanding from first principles across four languages (PyTorch, TypeScript, Rust, Julia), targeting engineers who want to move beyond tool usage to genuine comprehension.

What it actually is

  • What: An open-source, 416-lesson AI engineering curriculum spanning 20 phases, covering everything from linear algebra and backpropagation fundamentals through transformers, PyTorch, and autonomous agent systems. It is structured around a four-step learning loop: read the problem, derive the math, write the code, and run the test.
  • Who built it / maintained by: Rohit Kumaray (individual open-source contributor; exact GitHub handle unverified — name may be a transcription approximation from video audio)
  • Status: unknown
  • Why it matters: Addresses a gap in AI education where practitioners can use high-level AI frameworks and APIs without understanding the underlying mechanics (e.g., attention, backpropagation). The curriculum builds foundational understanding from first principles across four languages (PyTorch, TypeScript, Rust, Julia), targeting engineers who want to move beyond tool usage to genuine comprehension.
  • How it compares to alternatives:
  • fast.ai Practical Deep Learning
  • Andrej Karpathy's Neural Networks: Zero to Hero
  • deeplearning.ai specializations
  • CS231n (Stanford)
  • The Little Book of Deep Learning
  • GitHub stars: 0 · License: unknown · Archived: no

Links

  • (no links found)

Kickstarter guide

Search GitHub for the author's username (possibly a variant of 'rohitkumaray') and a repository named something like 'ai-engineering-curriculum'. Once found, clone the repo and begin with Phase 1, which covers linear algebra fundamentals. Follow the prescribed four-step method per lesson — read, derive, code, test — and progress linearly through the 20 phases toward advanced topics like autonomous agent swarms.