Neural Networks: Zero to Hero vs 
Awesome Machine Learning
Neural Networks: Zero to Hero vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. Karpathy builds backprop, then GPT, from scratch vs The reference index of ML libraries, by language.
Updated regularly · curated by olud.ai
Neural Networks: Zero to Hero vs Awesome Machine Learning at a glance
| Spec | Neural Networks: Zero to Hero | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Video course + code | Curated list |
| License | MIT | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | the single best way to truly understand deep learning | finding the right library in any language |
| GitHub stars | — | 73.6k |
How Neural Networks: Zero to Hero and Awesome Machine Learning score
| Criterion | Neural Networks: Zero to Hero | Awesome Machine Learning |
|---|---|---|
| Popularity | n/a | 4.5 |
| Maintenance | n/a | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 3.5 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
Neural Networks: Zero to Hero
Andrej Karpathy's legendary lecture series: you build automatic differentiation, then a language model, then GPT — writing every line yourself, with nothing hidden.
- Widely considered the best deep learning teaching ever made
- You implement backpropagation yourself — it finally clicks
- Ends with a working GPT you wrote line by line
Awesome Machine Learning
The long-standing curated index of machine learning frameworks, libraries and software, organised by programming language — the reference people have used for a decade.
- Maintained for over a decade
- Organised by language, not by hype
- The reference the whole field points to
Key differences
Neural Networks: Zero to Hero is video course + code, while Awesome Machine Learning is curated list. Their licenses differ (MIT vs CC0-1.0), which matters if you ship a commercial product. Neural Networks: Zero to Hero leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, Neural Networks: Zero to Hero fits the single best way to truly understand deep learning, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose Neural Networks: Zero to Hero for the single best way to truly understand deep learning. Choose Awesome Machine Learning for finding the right library in any language.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is Neural Networks: Zero to Hero or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while Neural Networks: Zero to Hero rewards more setup with more control.
Are Neural Networks: Zero to Hero and Awesome Machine Learning free?
Neural Networks: Zero to Hero is free and open source (MIT), and Awesome Machine Learning is free and open source (CC0-1.0). Neither charges for the core software.
Can I run Neural Networks: Zero to Hero and Awesome Machine Learning locally?
Neural Networks: Zero to Hero: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Neural Networks: Zero to Hero vs Awesome Machine Learning — which should I pick in 2026?
Choose Neural Networks: Zero to Hero for the single best way to truly understand deep learning. Choose Awesome Machine Learning for finding the right library in any language.
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