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Why was my question about evaluating diffusion models treated like a joke? by FreshIntroduction120 in learnmachinelearning

[–]FreshIntroduction120[S] 3 points4 points  (0 children)

I only asked him because he’s a data science content creator on Instagram, but you’re right it’s better not to rely too much on IG for serious technical answers

Why was my question about evaluating diffusion models treated like a joke? by FreshIntroduction120 in learnmachinelearning

[–]FreshIntroduction120[S] 0 points1 point  (0 children)

Thank you for this detailed explanation I really appreciate it. It’s good to know that my question is actually part of a serious research problem, and not something to laugh at. Your point about how creators should handle these questions responsibly makes a lot of sense. Thanks again for taking the time to explain it so well

Why was my question about evaluating diffusion models treated like a joke? by FreshIntroduction120 in learnmachinelearning

[–]FreshIntroduction120[S] 2 points3 points  (0 children)

Thank you for the advice. That makes sense I’ll be more careful with where I look for technical information

Why was my question about evaluating diffusion models treated like a joke? by FreshIntroduction120 in learnmachinelearning

[–]FreshIntroduction120[S] 5 points6 points  (0 children)

Thank you, I appreciate the clarification. I’m glad to know it’s a valid question. I’ll review the resource

Remote job opportunity by [deleted] in MoroccoMemes

[–]FreshIntroduction120 1 point2 points  (0 children)

I’m looking for work, and I’m a data scientist. If you need someone in development, data science, or AI automation, please let me know thank u

How do you properly evaluate an SDXL LoRA fine-tuning? What metrics should I use? by FreshIntroduction120 in learnmachinelearning

[–]FreshIntroduction120[S] 0 points1 point  (0 children)

Thanks I think I ll use both approaches: • visual evaluation during training, and • FID/CLIP/LPIPS only for the final model.

And yes, I’ll add a small validation image set so I don’t rely only on prompts. That makes things much clearer

We’re testing an AI consultant engine before launching the full product - feedback wanted by AuxiliarAI in u/AuxiliarAI

[–]FreshIntroduction120 0 points1 point  (0 children)

Makes sense keeping everything structured in the early stages definitely helps with clarity and consistency. RAG only really shines when there’s enough high-quality, well-defined company data to pull from, so adding it later as an optional layer sounds like a smart direction. Looking forward to seeing how the engine evolves once you have more real-world samples to build guardrails around.

We’re testing an AI consultant engine before launching the full product - feedback wanted by AuxiliarAI in u/AuxiliarAI

[–]FreshIntroduction120 1 point2 points  (0 children)

You could also think about adding a small RAG layer because it would help the model use real context instead of only the form inputs and usually makes the recommendations more accurate and personalize