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A perceptron is a simple artificial neuron that combines weighted inputs to make a binary classification and forms a foundation of neural networks.
k-NN predicts a new example from the labels or values of its closest stored neighbors in feature space.
The XOR problem is a classic machine learning example showing why neural networks need hidden layers and nonlinear activations for some patterns.
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.