NeuronStack.dev was born from a simple idea: to make advanced AI development more practical, understandable, and accessible for programmers. Founded in Toronto by an AI engineer with a passion for education, we publish articles, tools, and services for developers who want to learn neural networks without the buzzwords.
We focus on practical stacks like PyTorch, TensorFlow, ONNX, and more, combined with frontend/backend tools like React, FastAPI, and Docker. Each tutorial is hand-crafted and updated with real industry examples.
We help you assess model performance, ensuring that you have a robust solution ready for deployment.
We create in-depth tutorials and expert-level guides on AI, deep learning, and neural networks. Perfect for anyone who wants to dive deep into the subject.
Every tutorial is structured in a way that you can follow along, building your projects as you go, with clear, practical examples.
Our content covers a broad range of AI frameworks and tools including TensorFlow, PyTorch, Keras, and more. If it's part of the AI stack, we cover it.
Get started quickly with our pre-built project templates, including code snippets and full models ready for your own use.
We bridge the gap between academic knowledge and real-world programming. Our content doesn’t stop at “how models work” — we take you through full deployment pipelines, CI/CD, and API integration using modern stacks like Docker, FastAPI, and Node.js.
Every guide is written by experienced AI developers who understand the real challenges of working with neural networks. Plus, we open-source most of our code, so you can study it, build on it, or contribute.
Our philosophy is simple: clean code, no fluff. Whether it's PyTorch, TensorFlow, or Hugging Face Transformers, you get concise, well-structured articles with no clickbait — only actionable, production-quality examples.
We’re proudly based in Canada and tailor our content for Canadian developers — whether you're building a startup, applying for tech jobs, or experimenting with AI for your own projects. Local relevance, global quality.