Ai Engineering From Scratch
503 lessons, 20 phases, every algorithm built from raw math before any framework.
A rigorous, no-compromise curriculum that delivers on its promise: build every algorithm from scratch. If you have the time and background, this is one of the best ways to truly understand AI from the ground up.
- Self-taught engineers wanting deep AI literacy
- CS students seeking practical, foundational exposure
- Professionals transitioning into AI who want to understand internals
- Advanced developers tired of AI fluff and looking for substance
- Beginners looking for a quick, hand-holding course
- Users who prefer video-based learning
- Anyone wanting a framework-first approach (e.g., PyTorch from day one)
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip AI Engineering from Scratch if you need video guidance, hand-holding, or a framework-first approach—this curriculum is advanced, self-directed, and requires strong math and programming foundations.
This is completely free and open source (MIT-licensed). There are no paid tiers, making it the most affordable deep-learning curriculum available. Cheaper than any paid course, and more comprehensive than most free resources.
In short
Ai Engineering From Scratch — 503 lessons, 20 phases, every algorithm built from raw math before any framework. Best for Self-taught engineers wanting deep AI literacy, CS students seeking practical, foundational exposure, Professionals transitioning into AI who want to understand internals. Free to use.
Viability Score
How likely is Ai Engineering From Scratch to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 503 lessons from linear algebra to autonomous agents
- Every algorithm built from raw math first
- Code implementations in Python, TypeScript, Rust, Julia
- Lesson format: read problem, derive math, write code, run test, keep artifact
- 20 phases covering the full AI pipeline
- No framework dependencies until after manual implementation
- All content MIT-licensed and open source on GitHub
- No videos, no copy-paste deploys, no hand-holding
- Site generated from Markdown via build script, always in sync
- Glossary with key terms
- Roadmap for guided learning
- Community contributions via GitHub issues
- Self-hosted on local machine (clone repo)
- Supports progress tracking in browser (local storage)
- No paywall, no signup required
About Ai Engineering From Scratch
AI Engineering from Scratch is a free, open-source curriculum that systematically teaches core AI algorithms by building them from first principles. Spanning 503 lessons across 20 phases, the curriculum covers everything from linear algebra to autonomous swarms, with code in Python, TypeScript, Rust, and Julia. Maintained by Rohit Ghumare and community contributors, all content is MIT-licensed and hosted on GitHub. The website is plain HTML/CSS/JS, generated from Markdown to stay in sync. No signup, no paywall, no gated content. This is for engineers who want deep understanding over surface-level fluency.
Behind the Verdict
AI Engineering from Scratch is a standout resource for self-directed learners who want to truly understand AI internals. Its 503 lessons across 20 phases progress from linear algebra to autonomous swarms, with each algorithm built from raw math before introducing frameworks. The curriculum supports four languages (Python, TypeScript, Rust, Julia) and emphasizes a consistent loop: read, derive, code, test, artifact. There are no videos or hand-holding—just text and code. This approach ensures you can explain backpropagation, tokenization, attention, and agent loops, not just use them. The downsides are clear: it's advanced, assumes strong math and programming skills, and offers no grading or mentorship. Beginners will struggle. For experienced developers or CS students wanting deep AI literacy, this is a goldmine. For quick, framework-first learning, look elsewhere.
Researching Ai Engineering From Scratch? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Ai Engineering From Scratch actually fits — and what changes day-one when you adopt it.
You clone the repository, open Phase 1 (linear algebra), and start reading the first lesson. You derive the math on paper, then implement a matrix multiplication in Python without NumPy.
Outcome: After completing Phase 1, you have a solid foundation in linear algebra concepts needed for neural networks, and you've written your own matrix operations from scratch.
You are learning about backpropagation in class and want a deeper understanding. You find the relevant lesson in Phase 6, read the problem, derive the gradients manually, and implement backprop in TypeScript.
Outcome: You can now explain the chain rule behind backprop and reproduce it without a framework, giving you an edge in coursework and interviews.
You aim to build a custom chatbot. Instead of jumping to LangChain, you go through Phase 8 (tokenization), Phase 10 (attention), and Phase 12 (transformer from scratch). You implement each component in Rust.
Outcome: By the time you reach Phase 14 (agent loops), you understand the inner workings of the transformer and can debug agent behavior without black-box dependencies.
Use Cases
- Learn transformer attention by implementing it from scratch in Python
- Build a working backpropagation system without PyTorch or TensorFlow
- Develop an autonomous agent loop using raw math and logic
- Derive and code a tokenizer to understand encoding mechanics
- Follow the 20-phase roadmap to systematically cover AI from math to deployment
Limitations
- No interactive grading or mentorship is provided — learning is self-directed.
- The content is advanced and assumes strong mathematical and programming foundations; absolute beginners will struggle.
- No videos or guided walkthroughs exist; all lessons are text and code based.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Ai Engineering From Scratch tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Any learner—individuals, students, professionals—who wants complete, unrestricted access to all 503 lessons and materials without paying anything
What this tier adds
Free entry point: full access, no signup, no paywall, no restrictions. All content MIT-licensed.
Where the pricing makes sense
The company stage and team size where Ai Engineering From Scratch's pricing actually pencils out — and where peers do it cheaper.
This is completely free and open source (MIT-licensed). There are no paid tiers, making it the most affordable deep-learning curriculum available. Cheaper than any paid course, and more comprehensive than most free resources.
Setup time & first value
How long it actually takes to get something useful out of Ai Engineering From Scratch — broken out by persona, not the marketing-page minute.
For experienced developers: clone the repo and start lesson 1 in under 5 minutes. For beginners: expect to spend several hours reviewing prerequisite math and programming concepts before diving into the curriculum.
Resources & Guides
Official links
Tools that pair well with Ai Engineering From Scratch
Common stack mates teams adopt alongside Ai Engineering From Scratch, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Alternatives to Ai Engineering From Scratch
View allFrequently Asked Questions
Categories
Best-of guides
Used Ai Engineering From Scratch? Help shape our editorial sentiment research.