LLMs from Scratch vs 
Dive into Deep Learning
LLMs from Scratch vs Dive into Deep Learning compared for 2026 — features, license, ease of use, performance and which one to choose. Build a GPT from nothing, line by line vs The textbook where every equation is runnable.
Updated regularly · curated by OpenSourceAI.tech
LLMs from Scratch vs Dive into Deep Learning at a glance
| Spec | LLMs from Scratch | Dive into Deep Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Book + code | Interactive book |
| License | Apache-2.0 | CC-BY-SA-4.0 |
| Runs locally | Yes | Yes |
| Primary language | Python | Jupyter |
| Ease of use | Intermediate | Intermediate |
| Best for | genuinely understanding how an LLM works | a rigorous foundation you can actually execute |
| GitHub stars | 99k | 29.2k |
How LLMs from Scratch and Dive into Deep Learning score
| Criterion | LLMs from Scratch | Dive into Deep Learning |
|---|---|---|
| Popularity | 4.5 | 3.5 |
| Maintenance | 5.0 | 2.0 |
| Ease of use | 3.5 | 3.5 |
| 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
LLMs from Scratch
Sebastian Raschka's companion repository to "Build a Large Language Model (From Scratch)": you implement attention, a transformer, pretraining and fine-tuning yourself, in plain PyTorch.
- You build every component yourself — no black boxes
- Runs on a laptop, no cluster needed
- The clearest explanation of attention anywhere
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
Key differences
LLMs from Scratch is book + code, while Dive into Deep Learning is interactive book. Their licenses differ (Apache-2.0 vs CC-BY-SA-4.0), which matters if you ship a commercial product. In short, LLMs from Scratch fits genuinely understanding how an LLM works, and Dive into Deep Learning fits a rigorous foundation you can actually execute.
Which should you choose?
Choose LLMs from Scratch for genuinely understanding how an LLM works. Choose Dive into Deep Learning for a rigorous foundation you can actually execute.
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 LLMs from Scratch or Dive into Deep Learning easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are LLMs from Scratch and Dive into Deep Learning free?
LLMs from Scratch is free and open source (Apache-2.0), and Dive into Deep Learning is free and open source (CC-BY-SA-4.0). Neither charges for the core software.
Can I run LLMs from Scratch and Dive into Deep Learning locally?
LLMs from Scratch: yes · Dive into Deep Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LLMs from Scratch vs Dive into Deep Learning — which should I pick in 2026?
Choose LLMs from Scratch for genuinely understanding how an LLM works. Choose Dive into Deep Learning for a rigorous foundation you can actually execute.
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