# Deep Learning Wizard: a PyTorch tutorial repository you read on the web, not in the terminal

> Deep Learning Wizard is an MIT-licensed collection of PyTorch, LLM and data engineering notebooks and mkdocs markdown that powers a free tutorial site. It is a teaching resource, not a library, and the repository exists mainly to build that site.

**ritchieng/deep-learning-wizard** — Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.

- Repository: https://github.com/ritchieng/deep-learning-wizard
- Website: https://www.deeplearningwizard.com/
- Stars: 877 · Forks: 236
- Language: Python
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/ritchieng-deep-learning-wizard

## What Deep Learning Wizard actually is, and who it is for

The repository is the source of a website. The README states plainly: "This repository contains all the notebooks and mkdocs markdown files of the tutorials covering machine learning, deep learning, deep reinforcement learning, data engineering, general programming, and visualizations powering the website." The learning path the project intends is the one at www.deeplearningwizard.com, which the README calls mobile and tablet friendly. The repository is the raw material behind it.

The audience is a learner who wants a top-down route into PyTorch. The README describes the approach as top-down, meaning theory and code arrive together rather than after a long mathematical build-up. The published track runs from matrices and gradients, through linear and logistic regression, feedforward networks, CNNs, RNNs, LSTMs and autoencoders, then into improving models with learning rate scheduling, optimizers, weight initialization and activation functions. A separate language model track covers LLM hyperparameter tuning, multi-modal models, embeddings for retrieval augmented generation, and HPC containers with Apptainer. Machine learning and data engineering tracks sit alongside, using RAPIDS cuDF, cuML, pandas, scikit-learn, Cassandra and Delta Live Tables.

If you are looking for a package to import, this is the wrong shape of project. There is no library to install. The value is the written sequence and the notebooks behind it.

## How the repository is organised and how the site gets built

The top level holds .gitignore, LICENSE, README.md, deploy.sh, docs/, material/, mkdocs.yml and test.sh. That layout tells you the mechanism: mkdocs.yml is the configuration that assembles the markdown in docs/ into the published site, deploy.sh is the script that publishes it, and test.sh is the check that runs before or alongside that. The README says the notebooks and the mkdocs markdown files both live in the repository, so the notebook and the rendered page are two views of the same lesson.

This is a static site pipeline, not an application. There is no server process, no API and no runtime component to keep alive. The consequence cuts both ways. On the positive side, the cost of running the project is the cost of building a static site, which is close to nothing. On the negative side, everything that a normal software project gives you around a release is thin here. The README does not document rollback, does not describe a versioning policy for the tutorials, and does not explain how a reader is meant to pin the PyTorch version a given notebook was written against. The releases list shows how sparse the cadence is: v1.0.1 in 2019, v1.0.2 in 2023, v1.0.3 in 2024.

The material directory is not described in the README beyond appearing in the repository tree. If you want to know what it holds, you have to open it on the master branch.

## Cloning the repository and running a first PyTorch tutorial

The README does not give installation steps for the repository itself, because the intended entry point is the website. What follows is the minimum needed to get the source locally and work through the notebooks. Clone the default branch first.

```bash
git clone https://github.com/ritchieng/deep-learning-wizard.git
cd deep-learning-wizard
```

The README lists the libraries used across the tutorials: Python, PyTorch, Gym, NumPy and Matplotlib for the deep learning and reinforcement learning sections, and Python, PyTorch, Ollama, LlamaIndex, CUDA, Huggingface and Apptainer for the language model section. The README does not pin versions, so treat the environment as yours to assemble. A virtual environment keeps it isolated from anything else on the machine.

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install torch numpy matplotlib
```

Once that is in place, open the notebook for the topic you want. The matrices page is the documented starting point in the practical PyTorch progression, followed by gradients and then linear regression. If you would rather read than run, the same content is rendered at deeplearningwizard.com, which the README recommends as the place to start learning. The site is the faster path; the notebook is the one that lets you change the code and see what breaks.

For the container material, the README points to an HPC containers page built around Apptainer, and the repository carries apptainer in its topics list. The README does not provide the container build commands, so read that page before assuming a workflow.

## The maintenance gap is the real limitation

The repository is not archived, and the last push was on 2026-08-07, so the tree is being touched. That is not the same as the tutorials being current. The newest release is v1.0.3 from 2024-02-26, and the one before that, v1.0.2, is from 2023-10-03. Between v1.0.1 in 2019 and v1.0.2 in 2023 there is a four-year gap in tagged releases. A reader who picks a page has no release-level signal telling them which PyTorch generation it was written for.

That matters most in the sections where PyTorch has moved. The practical tutorials cover matrices, gradients, linear and logistic regression, feedforward networks, CNNs, RNNs, LSTMs, autoencoders and overcomplete autoencoders. The improving-models section covers learning rate scheduling, optimizers, and weight initialization and activation functions. These are exactly the areas where API details drift. The README does not promise version compatibility, and it does not describe a deprecation or update process for older notebooks.

There is a second, blunter limitation. The README says: "Take note this is an early work in progress, do be patient as we gradually upload our guides." That sentence is still in the README. The site's navigation lists more sections than the README describes in detail, and the README gives no completion status per page. If you are planning a course around this material, you cannot tell from the README alone which pages are finished and which are placeholders. Check each one on the site before you commit a syllabus to it.

The third limitation is scope. This is a teaching repository. There is no deployment tooling, no serving layer and no model registry, despite the project description mentioning deploying deep learning in production. The production-adjacent content in the README is the Numba speed optimization page and the Apptainer container page. If you need something to put a trained model behind an endpoint, this repository does not contain it.

## How it compares with a maintained course or a framework's own tutorials

The obvious alternative is the official PyTorch tutorials, which ship alongside the framework and are updated as the API changes. The difference in approach is structural rather than qualitative. Official tutorials are maintained by the people who change the API, so a tutorial that breaks is a bug in the same release cycle. Deep Learning Wizard is a third-party site built from a repository whose newest tagged release is from 2024, so a tutorial that breaks is a reader's problem until someone opens a pull request.

What the official material does not give you is the particular progression here: a single ordered path that starts at matrices and gradients, moves through regression and the standard network families, then continues into reinforcement learning with Gym, RAPIDS cuDF for GPU dataframes, and a language model track using Ollama, LlamaIndex and Huggingface. That breadth in one place, under one MIT licence, is the reason to pick this over a set of unrelated tutorials. The trade-off is that breadth bought with a slow release cadence means uneven currency across pages.

A second alternative is a paid video course, and the README acknowledges that route by pointing visual learners to a video course. The repository itself is the free written counterpart. If you learn better from video, the written material here is not a substitute, and the README does not claim it is.

## Licence and the cost of keeping a fork up to date

The repository is MIT licensed, and the README carries an MIT badge. MIT is permissive: you can reuse the notebooks and markdown in your own teaching, internal training or documentation, provided you keep the copyright notice and licence text with what you copy. The repository also carries a Zenodo DOI badge, which gives the project a citable identifier. This is a description of the licence, not legal advice; if you are republishing substantial portions, read the LICENSE file at the repository root and the terms that apply to any third-party dataset or library a notebook uses.

The upgrade cost is unusual for a software project because there is nothing to upgrade. You do not track a dependency version. You track the tutorial content, and the friction shows up when you pull from master and find a notebook whose PyTorch calls no longer match your installed version. The releases give you three anchor points (v1.0.1, v1.0.2, v1.0.3) and no changelog detail in the README. If you fork the material for internal use, the practical work is pinning your own environment and recording which PyTorch version each notebook you keep was verified against, because the repository does not do that for you. Contributions are invited: the README asks people to open a pull request for errors.

## Conclusion

Adopt Deep Learning Wizard if you want a free, MIT-licensed PyTorch progression that runs from matrices and gradients through CNNs, LSTMs and autoencoders, and you are willing to read it on deeplearningwizard.com rather than install it. Do not adopt it as a dependency, a maintained library, or a source of production deployment tooling: the README describes the repository as an early work in progress, the last push was on 2026-08-07, and the newest release is v1.0.3 from 2024-02-26. Before you rely on any page, check the corresponding notebook under docs/ or material/ on the master branch, confirm the PyTorch version it assumes, and verify that the section you need is not still listed as pending upload.

## FAQ

### What is Deep Learning Wizard?

It is an MIT-licensed repository of notebooks and mkdocs markdown that powers the deeplearningwizard.com tutorial site. The README describes the content as covering machine learning, deep learning, deep reinforcement learning, data engineering, general programming and visualizations.

### Is Deep Learning Wizard a Python package I can install with pip?

No. The README gives no install command for the project itself and points readers to the website to start learning. The repository holds the notebooks and markdown behind that site, so you clone it rather than pip install it.

### What PyTorch topics does Deep Learning Wizard cover?

The README lists practical PyTorch pages for matrices, gradients, linear regression, logistic regression, feedforward networks, CNNs, RNNs, LSTMs and autoencoders, plus an improving-models section on learning rate scheduling, optimizers, and weight initialization and activation functions.

## Sources

- [License: MIT](https://github.com/ritchieng/deep-learning-wizard/blob/master/LICENSE)
- [Project website](https://www.deeplearningwizard.com/)
- [README](https://github.com/ritchieng/deep-learning-wizard/blob/master/README.md)
- [Releases](https://github.com/ritchieng/deep-learning-wizard/releases)
- [ritchieng/deep-learning-wizard on GitHub](https://github.com/ritchieng/deep-learning-wizard)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/ritchieng-deep-learning-wizard