Dive into Deep Learning vs 
Applied ML
Dive into Deep Learning vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. The textbook where every equation is runnable vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
Dive into Deep Learning vs Applied ML at a glance
| Spec | Dive into Deep Learning | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Interactive book | Curated papers & posts |
| License | CC-BY-SA-4.0 | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Intermediate |
| Best for | a rigorous foundation you can actually execute | learning from what companies really did |
| GitHub stars | 29.2k | 29.9k |
How Dive into Deep Learning and Applied ML score
| Criterion | Dive into Deep Learning | Applied ML |
|---|---|---|
| Popularity | 3.5 | 3.5 |
| Maintenance | 2.0 | 2.0 |
| Ease of use | 3.5 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 3.5 | 5.0 |
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
Applied ML
Eugene Yan's curated collection of papers and engineering blog posts on how companies actually build and deploy ML systems in production — organised by problem, not by algorithm.
- Real production systems, not toy examples
- Organised by problem, not by algorithm
- Curated by a practising ML engineer
Key differences
Dive into Deep Learning is interactive book, while Applied ML is curated papers & posts. Their licenses differ (CC-BY-SA-4.0 vs MIT), which matters if you ship a commercial product. In short, Dive into Deep Learning fits a rigorous foundation you can actually execute, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose Dive into Deep Learning for a rigorous foundation you can actually execute. Choose Applied ML for learning from what companies really did.
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 Applied ML easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are Dive into Deep Learning and Applied ML free?
Dive into Deep Learning is free and open source (CC-BY-SA-4.0), and Applied ML is free and open source (MIT). Neither charges for the core software.
Can I run Dive into Deep Learning and Applied ML locally?
Dive into Deep Learning: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Dive into Deep Learning vs Applied ML — which should I pick in 2026?
Choose Dive into Deep Learning for a rigorous foundation you can actually execute. Choose Applied ML for learning from what companies really did.
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