Reactive Deep Learning: PyTorch Fundamentals in marimo Notebooks

[thliang01]

Proposal: Interactive Deep Learning with PyTorch and marimo

🎯 Executive Summary

I propose developing a comprehensive course that leverages marimo's reactive notebook paradigm to teach PyTorch fundamentals through interactive, reproducible examples. This course addresses the growing need for hands-on deep learning education while solving common pain points in traditional notebook-based teaching.

Issues #42

[Haleshot]

This is a great proposal indeed! I waited a while to respond to this issue (sorry about that) but it was done mainly waiting for our molab announcement (if you didn't have a chance to checkout molab already, our announcement blog here and the platform along with a relevant "Using PyTorch" demo notebook.

Could you also state any relevant resources that you will be referring to (mainly for a tentative proposed structure like it exists in other issues)?

[thliang01]

Hi @Haleshot! I've been thoroughly impressed by Molab's launch and have been actively exploring the "Using PyTorch" demo notebook. The cloud-based reactive environment represents a paradigm shift in deep learning education – addressing critical pain points that have long hindered interactive learning. As an enthusiastic marimo learn user, I'm excited to contribute to this transformative platform.

I propose developing a comprehensive "PyTorch Fundamentals" course that uniquely leverages marimo's reactive architecture to solve persistent challenges in deep learning education:

Key Differentiators:

• 🔄 True Reactivity: Live tensor operations with automatic dependency updates – learners see immediate cause-and-effect relationships

• ☁️ Zero-Friction Access: Eliminating CUDA/environment setup barriers that discourage 60% of beginners

• 🏭 Production-Ready Focus: Bridging the academia-industry gap with real-world deployment scenarios

• 📊 Integrated Visual Learning: Real-time model behavior visualization for intuitive understanding

📚 Resources

The course will synthesize best-in-class resources while adapting them to marimo's reactive paradigm:

Core References:

Deep Learning with PyTorch, Second Edition – Comprehensive theory and implementation

PyTorch in One Hour – Rapid multi-GPU training techniques

PyTorch Official Tutorials – Latest APIs and best practices

🎯 Proof of Concept

I've created two Molab notebooks.

Environment Verification

Pre-trained Networks Explorer

🤝 Collaboration & Next Steps

I'm eager to align this initiative with marimo's standards and roadmap. I would appreciate guidance on:

• Integration with the existing marimo learn curriculum structure

• Pedagogical approaches that align with marimo's interactive philosophy

• Technical requirements for course notebooks

I'm available to discuss details on marimo's Discord or continue in this GitHub issue.

Looking forward to contributing to marimo's mission of revolutionizing interactive computing education!

[Haleshot]

Just went through the two notebooks listed above; they look great (the preview ensures that things work).

I'm available to discuss details on marimo's Discord or continue in this GitHub issue.

I'd recommend starting this discussion (& tagging this GitHub issue) on discord as we have some more activity there. Also, there were two notebooks developed on Jax here which you might find interesting.

The references seem solid enough; would also recommend checkout out the PyTorch notebook that we provide on molab (worth checking out).

Also, setting up a poll in the courses channel for folks to contribute/recommend notebook topics for a course issue (as followed in other issues — a tentative proposed outline) would be a good way to get started.

Thanks for taking this initiative! This would be a great course to see the community work towards.