How do AI models learn from data?

How do AI models learn from data?

AI models learn by analyzing patterns in data through algorithms, adjusting their parameters to improve accuracy.

What types of data are best for training?

Diverse and high-quality data is best, including labeled datasets for supervised learning and varied examples for unsupervised learning.

Can you explain supervised vs unsupervised learning?

Sure! Supervised learning uses labeled data to train models, while unsupervised learning finds patterns in unlabeled data.

How do you evaluate the performance of a model?

Performance is evaluated using metrics like accuracy, precision, recall, and F1 score, depending on the task.

What are some common challenges in training AI models?

Common challenges include overfitting, underfitting, data quality issues, and computational resource limitations.