2025-12-29 12:22:39

Raw data piling up doesn't mean much. The true value lies in the data processing pipeline.

Perceptron Network's solution breaks down this process clearly: capturing raw signals → filtering valid inputs → structured processing → generating datasets usable by AI.

The key is not to pursue data volume, but rather the relevance, clarity, and practicality of the data. This logical flow, connected to production-level models, is what a real data pipeline should do.

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FrogInTheWell

· 2h ago

Data quality is the key; piling up garbage data is purely a waste of computing power.

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BTCBeliefStation

· 2h ago

What’s the use of piling up data? The key is how to process it --- I agree with this process; filtering + structuring is where the profit is --- Quality > Quantity, finally someone got it right --- The bottleneck for production-level models is this, the Perceptron approach is pretty good --- So all previous efforts were in vain? --- You really need to put effort into the data pipeline

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SerNgmi

· 3h ago

Garbage in, garbage out—that's true. Data cleaning is the real factor that makes a difference.

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HallucinationGrower

· 3h ago

Stacking data is useless, might as well carefully refine a set of processes.

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DAOdreamer

· 3h ago

Data cleaning is the key; piling up more junk data is useless.

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BearMarketSunriser

· 3h ago

Stacking data is useless; it depends on how you handle it. The idea of this Perceptron is indeed clear. --- Quality > Quantity. It’s about time to play it this way. I wonder how many projects are still desperately piling up data. --- A production-grade model is the real way to go. Having data alone is useless; it must be truly usable. --- Finally, someone has explained the entire process from signals to datasets thoroughly. --- Relevance and clarity—that’s the core of the data pipeline. I had it all backwards before.

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