{
  "id": 13648279,
  "title": "Training My First Neural Network on Windows with WSL 2 and PyTorch",
  "url": "https://urgent.news/2026/10/11/training-my-first-neural-network-on-windows-with-wsl-2-and-pytorch",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-11T05:13:48.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/lawson_dong/training-my-first-neural-network-on-windows-with-wsl-2-and-pytorch-3him"
  },
  "original_language": "en",
  "account": "Beginning with this tutorial, I aimed to explore the practical workflow behind training a neural network using Windows, WSL 2, Ubuntu, PyTorch, and plain Python. The goal was to set up a step-by-step guide for readers to follow and understand the setup process. The tutorial starts by outlining the tools necessary for this experiment: Windows terminal, WSL 2, Ubuntu, a Python virtual environment, PyTorch, and the project's directory structure. It then walks through the installation and configuration of each tool, providing clear instructions for each step.\n\nFirst, users need to have a Windows 10 or Windows 11 installation, with the ability to install WSL and an internet connection. The tutorial emphasizes that a GPU is not required for this specific experiment. The workflow then moves from launching the Windows terminal to installing and running WSL 2, which in turn sets up the Ubuntu Linux environment. Once inside Ubuntu, various system tools are installed using APT, including git, Python 3, pip, and nano. Following this, a project directory is created, and a virtual environment is set up using Python's venv module. PyTorch and NumPy are then installed within this Python environment, ensuring that the project has all the necessary dependencies.\n\nThe tutorial includes examples and verification steps for each installation and configuration step. For instance, after installing PyTorch, the reader is instructed to verify the installation by running a Python command that imports PyTorch and prints its version and creates a random tensor. This step-by-step format ensures that users can easily follow along and confirm that each part of the installation process is successful. Finally, a simple training script is provided to illustrate the XOR problem, one of the classic examples used to demonstrate neural networks. By creating a script that trains a model to recognize this pattern, readers can immediately see the results of their setup and the power of PyTorch.",
  "summary": "As a physics undergraduate beginning to explore AI research, I wanted to understand the practical workflow behind a neural network experiment: where the code lives, how the environment works, how to run training, and what gets saved afterward. This tutorial brings together my setup notes and first PyTorch experiment. We will build a small network for XOR, starting from a Windows computer and…",
  "key_points": [
    "Tutorial guides training neural network on Windows using WSL 2, Ubuntu, PyTorch",
    "Step-by-step setup includes Windows terminal, virtual environment, PyTorch installation",
    "XOR problem example demonstrates PyTorch functionality after setup"
  ],
  "editors_take": "This tutorial provides a comprehensive guide for training a neural network on Windows using WSL 2 and PyTorch, making the process accessible to readers without requiring a GPU.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}