ChatGPT:
A trained model learned using training data that includes data indicating the temperature of a liquid crystal display and usage environment data.
あなた:
ChatGPT:
"Training data" is the better choice in this context.
Why?
- "Training data" is the standard term in machine learning and artificial intelligence. It refers to the dataset used to train a model.
- "Learning data" is not commonly used in technical contexts and can sound unnatural.
So, the best translation remains:
👉 "A trained model learned using training data that includes data indicating the temperature of a liquid crystal display and usage environment data."
あなた:
ChatGPT:
"Learning data" is rarely used in technical contexts, but it could be appropriate in non-standard or informal contexts where the focus is on the general idea of data being used for learning, rather than in a strict machine learning sense.
Possible Contexts Where "Learning Data" Might Work:
- Educational contexts:
- If referring to data used by humans for learning rather than for training an AI model.
- Example: "The students analyzed various learning data to understand climate change patterns."
- General descriptions (non-technical):
- When speaking about any data that contributes to learning in a broad sense, not specifically AI training.
- Example: "The company collects customer feedback as learning data to improve its services."
- In some translations from Japanese:
- Since 学習データ is sometimes directly translated as "learning data," you might see it in non-technical or loosely translated materials. However, in professional machine learning contexts, "training data" is almost always the better term.
When to Avoid "Learning Data":
- In AI, machine learning, or deep learning discussions
- "Training data" is the industry-standard term.
- Example: ❌ "The model was trained using learning data." → ✅ "The model was trained using training data."
Conclusion:
Use "training data" in machine learning contexts. "Learning data" might work in informal, educational, or loosely translated contexts, but it's generally not the preferred term in technical writing.
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