I’m training a model, but the loss stops improving after a few epochs. I’ve tried changing the learning rate and training longer, but the results are still poor.

What are the first things you check when a deep learning model doesn’t seem to learn?

These are phrased to encourage discussion and experience-sharing, making them well suited for Reddit or a community forum.

I’m working with Alteryx and have a workflow that processes data from an Excel/CSV file. Right now, every time the source file is updated, I have to manually rerun the workflow.

I was wondering if there’s a way to have Alteryx automatically detect changes to the input file and rerun the workflow, or if I need to use something like Alteryx Scheduler, Windows Task Scheduler, or another workaround.

Has anyone implemented something similar? What approach worked best for you?

This keeps the question:

• Natural and Reddit-like.
• Focused on one problem.
• Open-ended enough to invite discussion and different solutions.

I’m working on a system where numerical values inside content are updated dynamically. The issue is that the title often contains the same number, and when the value changes in the content, the title becomes inaccurate.

I’m looking for a reliable way to automatically detect these changes and update the title accordingly without manually editing each post. Has anyone solved a similar problem or found a scalable approach for this?

Data interviews seem to be evolving fast. It’s no longer just SQL or case studies, now it’s system thinking, real-world problem solving, and even communication.

At the same time, a lot of candidates still feel uncertain going in.

From what you’ve seen:

• What’s actually being tested today?
• And what do candidates tend to worry about the most?

Would be interesting to hear both interviewer and candidate perspectives