As machine learning and deep learning (ML/DL) practitioners, we’re constantly experimenting, trying new approaches, and pushing the boundaries of what’s possible. But let’s be real: not every project is a success story. In fact, many of them fail.
So, where do we go to learn from these failures? Surprisingly, there aren’t many resources dedicated to sharing ML/DL failure stories. It’s an important question, and one that I think deserves more attention.
The Value of Failure
Failure is an integral part of the ML/DL development process. We learn from our mistakes, and they often lead to new insights and breakthroughs. But when we only focus on successes, we miss out on the opportunity to learn from our mistakes.
The Elusive Failure Repository
I’ve searched high and low for a platform or community where ML/DL practitioners can share their failure stories. Unfortunately, I couldn’t find anything substantial. There are some general repositories for research failures, but they’re not ML/DL-specific.
Why We Need a Failure Repository
Sharing failure stories can provide immense value to the ML/DL community. By learning from others’ mistakes, we can:
- Avoid common pitfalls: Identify common mistakes and avoid them in our own projects.
- Improve experimentation: Learn from others’ experimentation approaches and improve our own.
- Foster a culture of transparency: Encourage open discussion about failures and mistakes.
The Power of Community
Perhaps it’s time for us to create our own community or repository for sharing ML/DL failure stories. We could learn from each other’s experiences, and gain valuable insights from our collective mistakes.
So, I ask you: are you willing to share your ML/DL failure stories? Let’s create a space where we can openly discuss our mistakes and learn from each other.
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*Further reading: [Where can I see failures? : r/MLQuestions](https://www.reddit.com/r/MLQuestions/comments/1gsp7vk/where_can_i_see_failures/)*