Open-source deep learning courses, software, and research for coders
fast.ai is an open-source deep learning education platform for software developers learning practical AI and neural networks.
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fast.ai's primary offering is a set of free online courses, most notably 'Practical Deep Learning for Coders' and the newer 'How To Solve It With Code.' These courses are structured so that learners begin writing working models immediately, using real datasets, before diving into underlying theory. Course materials include video lectures, Jupyter notebooks, and accompanying written content.
The fastai library, built on top of PyTorch, provides a layered API that exposes high-level components for common deep learning tasks while allowing access to lower-level PyTorch primitives when needed. The project also maintains nbdev, a tool that enables software development directly inside Jupyter notebooks with full support for testing, documentation generation, and git-compatible version control. A companion textbook, 'Practical Deep Learning for Coders with fastai and PyTorch,' was published through O'Reilly and covers the same curriculum as the course.
fast.ai courses and software are aimed at working software developers and data practitioners who have coding experience but limited prior exposure to machine learning. All core courses and libraries are free. The Solveit platform, announced in late 2024 as part of fast.ai joining Answer.AI, is a separate interactive coding environment with its own feature set. Comparable open-source deep learning frameworks include PyTorch Lightning and Keras; comparable course platforms include Coursera's deeplearning.ai and Hugging Face courses.
The fastai library and nbdev are open-source and available on GitHub. The library targets Python environments and integrates directly with PyTorch. Courses are delivered via the web and use Jupyter notebooks, which can be run locally or on cloud platforms such as Google Colab and Kaggle Kernels.
A free online course covering disinformation, bias and fairness, ethical foundations, practical tools, privacy and surveillance, and algorithmic colonialism for those working in tech.
A course offering over 30 hours of video content covering deep learning foundations through advanced topics including Stable Diffusion, released as Practical Deep Learning for Coders Part 2.
A new course and educational platform from fast.ai (via Answer.AI) designed to make exploration and iterative development easier and faster through code-based problem solving.
A free course teaching deep learning through hands-on coding first, with theory introduced progressively, including a complete from-scratch rewrite covering modern deep learning techniques.
An educational platform with specific features designed to support exploration and iterative development, integrated with fast.ai's 'How to Solve it With Code' course.
A deep learning Python library built on PyTorch that provides a layered API with high-level components enabling practitioners to quickly achieve state-of-the-art results in standard deep learning tasks.
A Python library that makes it easy for users to download, verify, and extract archives, serving as a utility component within the fastai ecosystem.
A Python library that makes data transformations reversible and extensible through the power of multiple dispatch, enabling reversible pipelines in data science workflows.
A tool that enables software development directly within Jupyter notebooks, solving the Jupyter+git conflict problem and integrating with Quarto for documentation and productivity.
A conda mini-distribution focused on the PyTorch ecosystem that simplifies installation and updates of libraries such as PyTorch and related packages.
A 600-page book co-authored with O'Reilly covering deep learning with fastai and PyTorch, providing a companion reference to the free online courses.
Anyone with basic coding experience who wants to learn deep learning and AI. Fast.ai is a non-profit research group whose entire course catalog, software libraries, and community resources are provided at no cost.
Free, proven, and shipping — the rare education bet that pays back fast.
“fast.ai is a non-profit deep learning education platform built by Jeremy Howard and Rachel Thomas. Courses, the fastai PyTorch library, and nbdev are all free and actively maintained.”
Non-profit structure with O'Reilly book distribution, GitHub open-source libraries, and a 2024 merger into Answer.AI. That's a credible 36-month bet. No runway risk when there's no burn rate.
The tradeoff versus deeplearning.ai or Hugging Face courses: fast.ai skips certificates entirely. No credential for online completions. That's a hiring pipeline gap if you're using course completion as a proxy for readiness. But the coding-first structure means engineers ship working models before the theory lecture even starts — that's faster developer ramp than most alternatives.
Nbdev solving the Jupyter-git conflict problem is underrated. If your ML team lives in notebooks, that alone justifies adoption. Pilot this with three to five engineers on the Practical Deep Learning course. You won't need board approval for a $0 line item.
Peers using Coursera's deeplearning.ai get certificates; fast.ai gets faster practical ramp but no credential — depends on what your hiring signal needs.
Jeremy Howard co-founded the platform; O'Reilly published the companion book — this is a respected name in the ML community, not a risk.
Coding-first course structure means engineers run real models on real datasets in the first session, not week four.
Directly advances ML capability in engineering teams without displacing existing tooling — fastai sits on top of PyTorch, not beside it.
Non-profit with open-source libraries and Answer.AI backing as of 2024 — low failure risk, though roadmap velocity depends on a small founding team.
Engineering teams who want working ML practitioners faster than a theory-first curriculum delivers.
Your L&D program requires certified completions for compliance or hiring purposes.
The best free on-ramp to production PyTorch for developers who learn by doing.
“fast.ai's layered API and top-down pedagogy have trained more working practitioners than any comparable free resource. The strategic bet here isn't on the platform—it's on the PyTorch ecosystem it deepens.”
The fastai library's layered API is the real artifact worth evaluating. High-level abstractions for vision, text, and tabular tasks sit on top of raw PyTorch primitives, which means your team isn't locked into fastai's opinions—they can drop down when they need to. That's the right architecture for a training library. Comparable options like PyTorch Lightning take a similar philosophy but with a more enterprise-facing surface; fastai stays closer to the research-to-production seam.
nbdev is underrated. Solving the Jupyter-git conflict problem is a genuine quality-of-life win for teams where notebooks are first-class artifacts, not just scratchpads. The 30-plus hours in the Stable Diffusion course signals serious curriculum depth, not a surface-level intro.
The tradeoff is organizational: fast.ai is built for individual learners, not team onboarding pipelines. No certification, no admin tooling, no cohort management. If you're running structured upskilling across a data science org, you'll need to wrap this in your own LMS. For self-directed practitioners, that's irrelevant.
Free, PyTorch-native, and practitioner-focused puts it clearly above deeplearning.ai's paywalled Coursera tracks for cost-conscious engineering orgs.
Top-down coding-first structure matches how experienced developers actually learn fastest, but lacks team-level workflow tooling senior practitioners need.
Native PyTorch compatibility and Colab/Kaggle support cover most practitioner stacks; no enterprise SSO or LMS integration documented.
Betting on PyTorch is a safe three-year call; the Answer.AI acquisition adds Solveit as an evolving platform surface worth watching.
Layered PyTorch API with reversible transform pipelines via fasttransform shows library-grade thinking, not tutorial scaffolding.
Self-directed engineers and data practitioners who want production-grade PyTorch depth at zero cost.
You need structured cohort management or credentialed upskilling across a large data science org.
$0 sticker, $0 year 3 — but ROI measurement is entirely on you.
“fast.ai is fully free: courses, fastai library, nbdev, all of it. TCO math is simple; value proof is not.”
$0/seat × 50 × 12 = $0. No SSO tax. No overage clause. No auto-renewal trap. A non-profit research institute with no pricing page because there's nothing to price. Rare. The 600-page O'Reilly book is the one optional spend — call it $40-50 one-time per learner who wants it.
Real TCO lives in opportunity cost. 50 developers spending 40+ hours each on 'Practical Deep Learning for Coders' equals 2,000 labor hours. At $75/hour burdened cost, that's $150K in time. deeplearning.ai on Coursera charges $49-$79/month per seat but delivers structured accountability. fast.ai delivers depth and the fastai PyTorch library for production use — deeplearning.ai doesn't ship production tooling.
The tradeoff: ROI is unmeasurable without internal benchmarks. No certificate for online completions. No vendor SLA, no support contract, no procurement process. Procurement teams will love the invoice; finance teams will struggle to justify the headcount hours.
Zero procurement friction on cost; internal approval for 2,000+ labor hours is the actual process hurdle.
No contract, no auto-renewal window, no termination clause — open-source non-profit, exit whenever.
$0 across every tier, publicly visible, no sales call required — pricing page isn't needed when the price is zero.
No certificate for online completions and no built-in progress tracking makes skill ROI hard to measure internally.
Zero licensing cost; only real TCO is learner labor hours and optional $40-50 O'Reilly book per seat.
Engineering teams that want free, production-grade deep learning education with no procurement overhead.
Your organization needs certifiable outcomes or a vendor with contractual support obligations.
Free PyTorch wrapper that actually gets you training models on day one
“fastai's layered API over PyTorch is genuinely well-designed — high-level for fast iteration, drillable to raw primitives when you need them. The ecosystem around it (nbdev, fasttransform, the 30+ hour Part 2 course) is deeper than it looks from the outside.”
The fastai library's layered API is the real story here. You can call fit_one_cycle and have a trained ResNet in minutes, then drop into PyTorch-native code when the abstraction leaks. That's a design philosophy, not a happy accident. Keras does something similar but locks you closer to TensorFlow conventions. fastai stays honest about its PyTorch foundation.
nbdev is the sleeper feature. Notebook-to-git conflicts are a daily fight on any team doing exploratory ML work. The docs indicate nbdev2 fully resolves that problem, with Quarto integration for documentation generation. That's friction removed from a workflow that's usually full of it.
The tradeoff worth naming: fastai is a learning platform first, production library second. PyTorch Lightning has more enterprise adoption patterns, better multi-GPU ergonomics at scale. If you're fine-tuning for a research pipeline or learning deep learning seriously, fastai at $0 with 30+ hours of video content is hard to argue against. If you're deploying distributed training across a cluster, Lightning is the default.
High-level API gets you productive fast, but the gap between fastai convenience methods and production-ready PyTorch patterns requires bridging that the docs don't always walk you through.
Docs, courses, and the 600-page O'Reilly companion are written by people who clearly use the tools — code-first structure with theory introduced progressively is a practitioner choice, not a marketing one.
fastchan conda mini-distribution simplifies PyTorch ecosystem installs, which is a real friction point, but the Solveit platform (announced late 2024) adds ecosystem fragmentation to track.
The layered API makes advanced PyTorch access possible but discoverable depth past the course curriculum is less obvious — Part 2's 30+ hours of Stable Diffusion content is the clearest signal of ceiling.
Jupyter-native via nbdev, runs on Colab and Kaggle Kernels out of the box — fits the actual daily environment of most ML practitioners learning or prototyping.
Software engineers who can code but haven't trained neural networks and want the fastest legitimate path to working deep learning skills.
Your team is deploying distributed training pipelines and needs enterprise-grade MLOps patterns rather than an education-first library.
Free, coding-first, and genuinely better than most paid alternatives
“fast.ai is a free, non-profit deep learning education platform that gets working developers into real models before it touches theory. It's the anti-Coursera.”
Everything here is free. Not freemium, not 'free tier with 5 runs a month.' Free. The full Practical Deep Learning for Coders course, the fastai library built on PyTorch, nbdev, the ethics course — all of it. For a working developer who's been burned by deeplearning.ai's $49/month paywall, that lands differently.
The coding-first structure is the real differentiator. You're running models on real datasets in the first lesson. Theory comes after you've already seen it work. nbdev is a quiet gem too — it solves the Jupyter-plus-git problem that anyone who's tried to collaborate on notebooks knows is genuinely painful.
The tradeoff: this isn't a polished SaaS product. There's no changelog, no pricing page because there's nothing to charge. Mobile is web-only and probably rough. The Solveit platform, announced late 2024, is newer territory with unclear feature depth. But for a self-directed developer willing to work? Hard to beat at any price.
No changelog visible, no pricing page, sparse meta — the website feels maintained by researchers, not a product team.
The layered fastai API lets you stay high-level or drop into raw PyTorch, which means it scales with you across 30+ hours of Part 2 content.
Web-only delivery with Jupyter notebooks — mobile is almost certainly read-only at best, a non-starter for actual coding.
Coding-first structure means learners are running real models immediately, which is the opposite of the usual 'prerequisites' wall.
Relies on external environments like Google Colab and Kaggle Kernels, so reliability is partly borrowed from those platforms, not owned.
Working software developers with basic Python who want to build real deep learning skills without paying or waiting on prerequisites.
You need a structured, credentialed program with mobile access and hand-holding support.
Free, honest, 30+ hours deep — and the library actually ships
“fast.ai does what it says: free courses, real code first, theory later. No funding theater, no certificate paywall — just a non-profit that's been quietly shipping since 2016.”
Three green tells. One: tagline is 'Making neural nets uncool again' — that's self-aware, not hype. Two: the O'Reilly book exists, which means someone with editorial standards reviewed this curriculum. Three: nbdev solves a real, specific problem — git conflicts in Jupyter — not a made-up workflow pain.
The tradeoff is real though. This is education and tooling, not a managed platform. No API, no SLA, no changelog visible. The fastai library is PyTorch-dependent; if PyTorch moves, fastai follows. Compared to deeplearning.ai's Coursera offering, fast.ai gives you zero certificates for the free track — USF in-person students only.
Answer.AI acquisition in late 2024 is the watch item. Non-profits merging into new entities can drift. The Solveit platform is early and thin on public detail. But 'Practical Deep Learning for Coders' with 30+ hours of video and an active forums community is durable. This isn't vaporware.
Code-first pedagogy is a genuine gap vs. deeplearning.ai's theory-heavy Coursera path, but Hugging Face courses are closing that distance fast.
It's all open-source on GitHub, PyTorch underneath — if fast.ai disappears tomorrow, the notebooks and library remain forkable.
Non-profit structure and Answer.AI merger add uncertainty; no public changelog visible, and Solveit platform details are thin as of late 2024.
Tagline is deliberately anti-hype; pricing page is honest that certificates require in-person USF enrollment — no dark patterns.
Jeremy Howard and Rachel Thomas have shipped courses, a library, nbdev, and an O'Reilly book — pattern matches durable open-source educator, not abandoned MOOC.
Working developers who want practical PyTorch skills without a paywall or theory-first gatekeeping.
You need employer-recognized credentials or a managed platform with SLAs and support.
Common questions answered by our AI research team
Yes, fast.ai courses are free. The product description explicitly identifies them as free courses.
fast.ai uses PyTorch as its deep learning framework, accessed through the fastai Python library built on top of PyTorch.
nbdev solves the git conflict problem with Jupyter notebooks. Previously, using git with Jupyter could create conflicts and break notebooks; nbdev2 fully resolved this issue.
fast.ai currently offers two courses: Practical Deep Learning for Coders and How to Solve it With Code. A book companion (Practical Deep Learning for Coders with fastai and PyTorch) is also available.
No. fast.ai structures courses around hands-on coding first, with theory introduced progressively rather than as a prerequisite.
fast.ai is a non-profit research institute based in San Francisco that publishes free online deep learning courses and develops the open-source fastai PyTorch library.
