What are some common mistakes in AI training?

What are some common mistakes in AI training?

Some common mistakes include using biased data, not enough training data, and overfitting the model.

How can I avoid biased data in my training?

To avoid biased data, ensure diversity in your dataset and regularly audit it for fairness.

What is overfitting and how can I prevent it?

Overfitting occurs when a model learns noise instead of the signal. You can prevent it by using techniques like cross-validation and regularization.

Can you give examples of good training data?

Good training data is representative, diverse, and well-labeled. For instance, a balanced dataset for image recognition should include various angles and lighting conditions.