Hi everyone,
I’m looking for a few people who would like to collaborate on a neural network project. The course encourages group learning, so I think working together could help all of us understand the concepts better and build something genuinely useful—not just another basic demo.
sure, I would like to try and build something with you and a group.
Hi Mildrien,
That’s great to hear — thanks for your interest. I’d be glad to collaborate.
Since you’re open to building something as a group, I’d love to hear your suggestions on what kind of neural network project you think would be most valuable or interesting to work on together. I’m open to ideas that go beyond basic demos and focus on real learning and practical implementation.
Looking forward to your thoughts.
Hello @AliKhan3 I would also like to collaborate on an AI project as a group. I’ve already tried to do something on my own: GitHub - Fieldy76/Agentic-Travel-Planner: A production-ready, framework-free Agentic Workflow for travel planning built with Python and the Model Context Protocol (MCP).
Feel free to have a look. I am really open to everything interesting on this topic 
Let’s work backwards together. I have this idea of using a fine-tuned model with access to the user’s personal library account free online-resource access to: (LinkedIn, O’Reilly content, consumer guides), expressing the content via a HeyGen avatar. Or using an augmented format for later smart glasses hands-on real-time instructions for reverse engineering/troubleshooting/building. Just some ideas I’d like to work on—but what would you recommend as a good group project?Do you know anything about NoyRon from leap71 or have any interest with qbraid for possible hybrid quantum classical computing?hope to hear from you, happy holiday’s.
For a collaborative project, I would suggest working on a Kaggle competition as a focused and practical way to learn together and build something tangible. One good option is the Deep Past Initiative – Machine Translation Challenge, which provides a well-scoped NLP problem and strong learning value around data preprocessing, modeling, and evaluation. Alternatively, challenges like the AI Mathematical Olympiad (Progress Prize) or even completed competitions such as the Google Tunix Hackathon can still be very useful for studying solutions, experimenting with architectures, and learning from real-world constraints in a collaborative setting.
I’m now looking forward to active collaboration—setting up a small group on Discord or LinkedIn so we can coordinate, divide tasks, and start implementing rather than keeping things purely theoretical.
I apologize that I may have overstepped the conversation in wanting to participate in a group project. Unfortunately, Kaggle is not my thing. I tried it out in 2016 when Jeremy Howard was Kaggle King while teaching fastAi. I’m sorry that you may have missed out on their 5-day Google Boot Camp at the beginning of December 2025. But I don’t feel like pasting and copying the crowd. If you can, please research QBraid hybrid classical quantum neural network VQE RLAI self-evolving avatar system utilizing smart glasses as a user interface. I think that might be the future, besides competing for profit on Kaggle.
I appreciate the clarification and your perspective. I don’t currently have deep hands-on experience with hybrid quantum–classical systems, VQE, or qBraid, but I’m genuinely interested in learning and exploring this direction. The idea of combining agent-based AI, reinforcement learning, and emerging quantum workflows—especially with embodied interfaces like smart glasses—sounds both challenging and forward-looking.
If you’re open to it, I’d be glad to start at a conceptual level, study the fundamentals you mentioned, and gradually contribute as we shape a small, exploratory prototype or research direction together. I’m very much open to learning from scratch and committing time to understand the system end-to-end rather than treating it as a superficial experiment.