Dive into Deep Learning vs 
Awesome Machine Learning
Dive into Deep Learning vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. The textbook where every equation is runnable vs The reference index of ML libraries, by language.
Updated regularly · curated by OpenSourceAI.tech
Dive into Deep Learning vs Awesome Machine Learning at a glance
| Spec | Dive into Deep Learning | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Interactive book | Curated list |
| License | CC-BY-SA-4.0 | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | a rigorous foundation you can actually execute | finding the right library in any language |
| GitHub stars | 29.3k | 73.8k |
How Dive into Deep Learning and Awesome Machine Learning score
| Criterion | Dive into Deep Learning | Awesome Machine Learning |
|---|---|---|
| Popularity | 3.5 | 4.5 |
| Maintenance | 2.0 | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 3.5 | 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
Dive into Deep Learning
An open textbook used in 500+ universities: every concept comes with maths, runnable code and exercises, available for PyTorch, TensorFlow, JAX and MXNet.
- Adopted by 500+ universities worldwide
- Every equation has runnable code beside it
- Works with PyTorch, TensorFlow and JAX
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
Dive into Deep Learning is interactive book, while Awesome Machine Learning is curated list. Their licenses differ (CC-BY-SA-4.0 vs CC0-1.0), which matters if you ship a commercial product. Dive into Deep Learning leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, Dive into Deep Learning fits a rigorous foundation you can actually execute, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose Dive into Deep Learning for a rigorous foundation you can actually execute. 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 Dive into Deep Learning or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while Dive into Deep Learning rewards more setup with more control.
Are Dive into Deep Learning and Awesome Machine Learning free?
Dive into Deep Learning is free and open source (CC-BY-SA-4.0), and Awesome Machine Learning is free and open source (CC0-1.0). Neither charges for the core software.
Can I run Dive into Deep Learning and Awesome Machine Learning locally?
Dive into Deep Learning: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Dive into Deep Learning vs Awesome Machine Learning — which should I pick in 2026?
Choose Dive into Deep Learning for a rigorous foundation you can actually execute. Choose Awesome Machine Learning for finding the right library in any language.
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