A modified cross-entropy loss that down-weights easy examples, helping with class imbalance.
A collection of data examples used for training, validating, or testing machine learning models.
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 dataset where classes have significantly different numbers of examples, causing models to bias toward majority classes.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
A function that turns a list of scores (logits) into probabilities that are all positive and sum to 1; the standard output of classifiers and language models.
A loss function for classification that measures the difference between predicted and true probability distributions.
A modified cross-entropy loss that down-weights easy examples, helping with class imbalance.
This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.