A metric measuring overlap between predicted and ground truth segmentations, common in medical imaging.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.
Classifying every pixel in an image into categories, creating a pixel-level understanding of scenes.
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.
The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.
The harmonic mean of precision and recall, providing a single metric that balances both concerns.
A metric measuring overlap between predicted and ground truth segmentations, common in medical imaging.
This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.