minGPT vs Neural Networks: Zero to Hero
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A small, readable PyTorch re-implementation of GPT for training and running language models.
Open source · MIT
A free video course that builds neural networks and GPT-style language models from scratch in Jupyter notebook
The short answer
Choose minGPT if you need…
- Students who want to read a working GPT implementation end to end
- Developers who want a small, hackable base for experimenting with transformer models
- People who want to load GPT-2 weights and generate text with PyTorch
- Researchers who prefer a minimal codebase over a large framework
Choose Neural Networks: Zero to Hero if you need…
- Learners with basic Python who want to understand how neural networks train
- People who prefer following along with video and notebooks
- Students who want to see backpropagation and GPT implemented by hand
- Developers curious how tokenizers and language models work internally
Side-by-side
| minGPT | Neural Networks: Zero to Hero | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 25k | 25.2k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Jupyter Notebook |
| Last code update | 2 years ago | 2 years ago |
What each one is
minGPT
minGPT is a compact PyTorch codebase that reimplements the GPT transformer model, covering both training and inference. The README describes it as roughly 300 lines of model code, split into a model file, a byte pair encoder, and a generic training loop, aimed at being easy to read and learn from. It is a library you import into your own Python project, with demos for tasks like addition and character-level text generation.
Neural Networks: Zero to Hero
This is a course on neural networks that starts from the basics. It is a series of YouTube videos in which the author writes and trains neural networks step by step, and the notebooks built in those videos are stored in this repository. Each lecture covers a topic such as backpropagation, language modeling, batch normalization, and the GPT transformer architecture.
Best for
minGPT
- Students who want to read a working GPT implementation end to end
- Developers who want a small, hackable base for experimenting with transformer models
- People who want to load GPT-2 weights and generate text with PyTorch
- Researchers who prefer a minimal codebase over a large framework
Neural Networks: Zero to Hero
- Learners with basic Python who want to understand how neural networks train
- People who prefer following along with video and notebooks
- Students who want to see backpropagation and GPT implemented by hand
- Developers curious how tokenizers and language models work internally
Strengths
minGPT
- Small enough to read in an afternoon, with the model in about 300 lines according to the README
- Clear separation between model, tokenizer, and training loop
- MIT licensed and widely referenced in notebooks, blogs, and courses
Neural Networks: Zero to Hero
- Free, MIT-licensed material with notebooks you can run and modify
- Builds models step by step, showing the reasoning behind each part
- Covers a progression from basic backpropagation to GPT and tokenization
Not ideal for
minGPT
- People who need an actively maintained project: the README says minGPT is in a semi-archived state and points to nanoGPT for newer work
- Anyone needing distributed training or mixed precision, which the README lists as todos
- Users looking for a ready-made chatbot or polished end-user app
Neural Networks: Zero to Hero
- Readers who want a text-only reference, since the README points to YouTube videos for the explanations
- People with no Python experience, since lecture 1 assumes basic Python and some calculus recollection
- Anyone looking for a finished, production-ready library; the README says the course 'may grow into something more respectable' and describes itself as ongoing
Trade-offs
minGPT
- Semi-archived as of January 2023; the author suggests nanoGPT for newer development
- Test coverage is described as limited, and there is no requirements.txt yet per the README
- Lacks mixed precision and distributed training, which are listed as future todos
Neural Networks: Zero to Hero
- Explanations live mainly in videos, so the README alone gives limited detail
- The README says the course is ongoing and some topics are marked as not yet covered
- Depends on PyTorch and Jupyter, which the README does not set up in detail
Getting started
minGPT
Clone the repository
git clone https://github.com/karpathy/minGPT.gitEnter the directory
cd minGPTNeural Networks: Zero to Hero
Clone the repository
git clone https://github.com/karpathy/nn-zero-to-heroFrequently asked questions
What is the difference between minGPT and Neural Networks: Zero to Hero?
minGPT: A small, readable PyTorch re-implementation of GPT for training and running language models. Neural Networks: Zero to Hero: A free video course that builds neural networks and GPT-style language models from scratch in Jupyter notebook. minGPT suits students who want to read a working gpt implementation end to end, while Neural Networks: Zero to Hero suits learners with basic python who want to understand how neural networks train.
Are minGPT and Neural Networks: Zero to Hero free?
minGPT: Free and open source. Neural Networks: Zero to Hero: Free and open source.
Is minGPT or Neural Networks: Zero to Hero more popular?
Neural Networks: Zero to Hero has more GitHub stars (25.2k) than minGPT (25k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to minGPT and Neural Networks: Zero to Hero?
Other options people compare them with include llm.c, micrograd.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.