pyre-code helps you learn machine learning. You practice core concepts through 68 coding problems. These problems range from basic math like ReLU functions to advanced topics like flow matching, attention mechanisms, reinforcement learning from human feedback, and diffusion models. You receive feedback on your code in your web browser.
You need a computer running Windows 10 or Windows 11. Your computer should have at least 8 GB of memory to run the platform without issues. We recommend a solid-state drive for faster performance. You also need a web browser such as Chrome, Edge, or Firefox.
Visit this page to download the software.
- Navigate to the link above.
- Select the code button.
- Choose the option to download the ZIP file.
- Save the file to your computer.
- Create a folder on your desktop for the project files.
- Open the downloaded ZIP file.
- Move all contents of the zip folder into the folder you created on your desktop.
The application requires Python to run the machine learning exercises.
- Go to the official Python website.
- Download the latest installer for Windows.
- Run the installer file.
- Check the box that says "Add Python to PATH" before you click install. This step is important for the software to work correctly.
- Wait for the installation to finish.
- Open the Command Prompt on your computer. You can find this by typing "cmd" into the Windows search bar.
- Type
python --versionand press Enter to verify the installation. You should see a version number appear.
Once you set up Python, follow these steps to launch the platform.
- Open your Command Prompt.
- Type
cdfollowed by a space. - Drag your project folder from your desktop into the Command Prompt window. This action fills in the path to your folder.
- Press Enter.
- Type
pip install -r requirements.txtand press Enter. This command downloads the tools needed for the platform. - Type
python app.pyand press Enter. - Open your web browser.
- Type
http://localhost:5000into the address bar and press Enter.
The pyre-code interface appears in your browser. You can select a problem from the list and begin coding.
The platform organizes problems by difficulty and topic. You start with foundational concepts and move toward modern techniques.
You learn how to build the building blocks of neural networks. You write functions for activation layers like ReLU and Sigmoid. These exercises teach you how data moves through a model.
Large language models rely on attention. You create code that trains a model to focus on specific parts of an input sequence. You see how this logic powers modern translation and text generation tools.
You explore ways for software to learn through trial and error. You use reward signals to train a model to make decisions in a virtual environment.
You write code that generates images from noise. This topic covers the math behind current artistic generation software. You see the step-by-step process of turning random patterns into clear pictures.
The platform provides instant feedback. When you finish a problem, the system checks your code against expected results. If your code fails, the platform displays an error message explaining the math or logic issue. Use these suggestions to refine your approach.
Practice is the best way to understand these concepts. Complete one problem every day to build your knowledge. If you get stuck, review your previous work to see how you solved earlier tasks.
If the software does not start, check these common items:
- Verify that you installed Python correctly by re-running the installer and checking the PATH option.
- Make sure you installed the requirements. The command
pip install -r requirements.txtmust run successfully without errors. - Ensure no other programs use port 5000. If you have other web development tools running, close those programs before you start the platform.
- Check your internet connection. Some initial setups require a connection to download additional libraries needed for the exercises.
The project receives regular updates. To get the newest problems:
- Delete your current folder.
- Visit the download page again.
- Download the latest version of the code.
- Follow the setup instructions provided in this guide again.
Your progress saves within your browser cache, but you may want to back up your code in a separate file if you wish to keep a permanent record of your work.