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A parameter is a learned numerical value, such as a neural network weight, that determines how an AI model transforms input into output.
Logistic regression estimates the probability of a class by applying a logistic function to a weighted combination of features.
Interpretability is the degree to which people can understand how a model uses inputs to produce a prediction or decision.
Optimization finds model parameters or decisions that best satisfy an objective while respecting practical resource and safety constraints.
Linear regression predicts a continuous value using a weighted linear relationship between input features and the target.
Hyperparameter tuning searches for training and model settings that deliver strong performance on validation data.
XLM-R is a multilingual transformer encoder that supports cross-lingual classification, entity recognition, and language understanding tasks.
Optical character recognition (OCR) is an AI technology that converts text in images, scans, photos, and PDFs into searchable machine-readable data.
Hybrid AI combines different artificial intelligence approaches, often linking machine learning with rules, search, or symbolic reasoning.
Explainable AI uses methods that help people understand, evaluate, and challenge how an AI system reaches a decision or prediction.
One-shot learning is an AI approach that recognizes a new task or category from one example by using knowledge learned during previous training.
Normalization transforms data or neural network activations into a consistent scale or distribution to support stable and efficient AI model training.
XGBoost is a gradient-boosting algorithm that combines decision trees for accurate classification, regression, ranking, and forecasting.
Named entity recognition (NER) is an NLP task that finds and classifies people, places, organizations, dates, products, and other entities in text.
An embedding is a numerical representation that places related words, images, documents, or other items close together in a mathematical space.