micrograd 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 tiny educational autograd engine and small neural net library with a PyTorch-like API.
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 micrograd if you need…
- Students learning how backpropagation works under the hood
- Developers who want to read a complete autograd implementation in a short codebase
- Educators demonstrating neural net training on small examples like binary classification
- Anyone curious about how PyTorch-style APIs might be built
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
| micrograd | Neural Networks: Zero to Hero | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.6k | 25.2k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Jupyter Notebook | Jupyter Notebook |
| Last code update | 3 months ago | 2 years ago |
What each one is
micrograd
micrograd is a very small Python library that performs automatic differentiation, the calculus step that lets neural networks learn. It works on single scalar values, breaking every operation into tiny adds and multiplies, and it includes a small neural network layer on top with an API similar to PyTorch. The README describes it as potentially useful for educational purposes, and it is meant to be read and understood rather than used for large-scale work.
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
micrograd
- Students learning how backpropagation works under the hood
- Developers who want to read a complete autograd implementation in a short codebase
- Educators demonstrating neural net training on small examples like binary classification
- Anyone curious about how PyTorch-style APIs might be built
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
micrograd
- Very small codebase, which the README says is about 100 lines for the engine and 50 for the nn library
- Includes runnable notebooks for training and graph visualization
- MIT licensed and easy to install with pip
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
micrograd
- Training large models, since it operates on scalar values one at a time and the README does not claim performance for big workloads
- Production machine learning pipelines, where mature frameworks such as PyTorch are the usual choice
- Users who need GPU or tensor-level computation, which the README does not mention
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
micrograd
- Scalar-only operations make it far too slow and limited for real-world model sizes
- Running the tests requires installing PyTorch separately
- The README is brief and offers little beyond the example code and notebooks
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
Neural Networks: Zero to Hero
Clone the repository
git clone https://github.com/karpathy/nn-zero-to-heroFrequently asked questions
What is the difference between micrograd and Neural Networks: Zero to Hero?
micrograd: A tiny educational autograd engine and small neural net library with a PyTorch-like API. Neural Networks: Zero to Hero: A free video course that builds neural networks and GPT-style language models from scratch in Jupyter notebook. micrograd suits students learning how backpropagation works under the hood, while Neural Networks: Zero to Hero suits learners with basic python who want to understand how neural networks train.
Are micrograd and Neural Networks: Zero to Hero free?
micrograd: Free and open source. Neural Networks: Zero to Hero: Free and open source.
Which is easier to set up, micrograd or Neural Networks: Zero to Hero?
micrograd is easier to get started with (rated beginner); Neural Networks: Zero to Hero is rated intermediate and usually needs more setup.
Is micrograd or Neural Networks: Zero to Hero more popular?
Neural Networks: Zero to Hero has more GitHub stars (25.2k) than micrograd (18.6k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to micrograd and Neural Networks: Zero to Hero?
Other options people compare them with include tinygrad, minGPT.
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.