Machine Learning

Named entity recognition

Named entity recognition (NER) is the natural language processing task of locating spans of text that name real-world things, such as people, organizations,...

Natural Language Processing

Natural Language Understanding

Natural language understanding (NLU) is the branch of artificial intelligence and computational linguistics that enables machines to read human language and...

Natural Language Processing

Natural language processing

Natural language processing (NLP) is the branch of artificial intelligence that enables computers to read, understand, generate, and respond to human language...

Artificial IntelligenceNatural Language Processing

Negative class

In binary classification, the negative class is the outcome the model treats as the default or "no" result: the label assigned to instances that do not possess...

Netflix Prize

The Netflix Prize was an open machine learning competition, run by Netflix from October 2, 2006 to September 21, 2009, that offered US$1,000,000 to the first...

AI EventsAI History

NeurIPS

NeurIPS (the Conference on Neural Information Processing Systems) is the largest and one of the most prestigious academic conferences in artificial...

Artificial Intelligence

Neural Network

A neural network (also called an artificial neural network or ANN) is a computational model, loosely inspired by the networks of biological neurons in animal...

Deep LearningNeural Networks

Neural architecture search

Neural architecture search (NAS) is a technique for automating the design of neural network architectures. Rather than relying on human experts to hand-craft...

Deep LearningMLOps

Neuron

A neuron (also called a node or unit) is the fundamental computational element of an artificial neural network: it takes one or more numeric inputs, multiplies...

Neural Networks

Node (decision tree)

A node is the basic building block of a decision tree: it is a single point in the tree that is either a condition (an internal node that tests a feature and...

Noise

Noise in machine learning is any unwanted, irrelevant, or random variation in data that obscures the true underlying patterns a model is trying to learn. It is...

Data & Datasets

Non-Response Bias

Non-response bias is the error that arises when the people or units that do not respond to a survey, study, or data collection process differ systematically...

Data & DatasetsStatistics

Non-binary condition

In decision tree learning, a non-binary condition is a test at a node that has more than two possible outcomes, routing each example to one of three or more...

Nonlinear

Nonlinear describes any function, model, or relationship that does not satisfy the property of linearity. In machine learning the term most often refers to the...

Mathematics

Nonstationarity

Nonstationarity refers to the condition in which the statistical properties of a data-generating process change over time. In a stationary process, quantities...

Statistics

Normalization

Normalization is the process of scaling numerical data to a standard range or distribution so that features and activations are comparable and downstream...

Data & Datasets

Novelty Detection

Novelty detection is a branch of machine learning concerned with identifying test data that differ in some meaningful way from the data available during...

NumPy

NumPy (short for Numerical Python) is the foundational open-source library for numerical and scientific computing in Python, providing an n-dimensional array...

AI Tools & ProductsMathematics

Numerical Data

Numerical data (also called quantitative data) is information expressed as numbers on a continuous or discrete scale that supports arithmetic operations such...

Data & Datasets

OCR Models

OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through...

Artificial IntelligenceComputer Vision

ONNX

ONNX (Open Neural Network Exchange) is an open standard file format for representing machine learning models so they can be moved between different frameworks,...

AI Tools & Products

ORPO

ORPO (Odds Ratio Preference Optimization) is a preference alignment algorithm for large language models that merges supervised fine-tuning and preference...

Large Language ModelsTraining & Optimization

Object detection

Object detection is a computer vision task that locates and classifies every object instance in an image or video frame, returning for each one a bounding box,...

Artificial IntelligenceComputer Vision

Objective

See also: Machine learning terms In machine learning, an objective (or objective function) is the scalar function that a learning algorithm optimizes during...

Training & Optimization

Oblique condition

An oblique condition is a decision tree split test that involves more than one feature, comparing a linear combination of several numerical features to a...

Offline

In machine learning, offline describes operations that happen ahead of time on a fixed dataset rather than continuously on live data. Google's Machine Learning...

One-Hot Encoding

See also: Machine learning terms One-hot encoding is a data preprocessing technique that converts a categorical variable with distinct categories into ...

Data & Datasets

One-Shot Learning

One-shot learning is a machine learning approach in which a model learns to recognize or classify new categories from only a single labeled example per class....

Deep Learning

One-vs.-all

See also: Machine learning terms One-vs.-all (OvA), also known as one-vs.-rest (OvR) or one-against-all, is a strategy for turning a multi-class classification...

Online learning

See also: Machine learning terms Online learning is a machine learning paradigm in which a model receives data sequentially, one example or one mini-batch at a...

Reinforcement Learning

Open-source AI

Open-source AI is artificial intelligence released so that its weights, code, or both can be used, inspected, modified, and redistributed by anyone. The term...

Artificial IntelligenceOpen Source AI

OpenThoughts

OpenThoughts is an open-source initiative and a series of datasets of verified reasoning traces created to train open reasoning models. Launched on January 28,...

Data & Datasets

Optimizer

An optimizer in machine learning is an algorithm that iteratively adjusts a model's learnable parameters to minimize (or maximize) an objective function,...

Deep LearningTraining & Optimization

Out-Group Homogeneity Bias

Out-group homogeneity bias, also called the out-group homogeneity effect, is the cognitive bias in which people perceive members of an out-group as more...

AI Ethics

Out-of-bag evaluation (OOB evaluation)

See also: Machine learning terms, Bagging, Random forest, Cross-validation Out-of-bag (OOB) evaluation, sometimes called out-of-bag estimation or OOB error, is...

Model Evaluation

Outlier Detection

Outlier detection is the process of identifying data points, observations, or patterns that deviate so markedly from the rest of a dataset that they are likely...

Data & DatasetsStatistics

Outliers

See also: machine learning terms, anomaly detection, robust statistics, data preprocessing An outlier is a data point that differs so markedly from the rest of...

Statistics

Output Layer

See also: neural network, activation function, loss function, hidden layer, softmax, backpropagation The output layer is the final layer of a neural network:...

Deep LearningNeural Networks

Overfitting

Overfitting is when a machine learning model fits its training set so closely that it learns the noise and quirks of those specific examples instead of the...

Deep LearningModel Evaluation

Oversampling

Oversampling is a data preprocessing technique in machine learning that fixes class imbalance by increasing the number of minority class examples in the...

Data & Datasets

PASCAL VOC

PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) is a long-running benchmark dataset and annual challenge...

AI BenchmarksComputer Vision

PR AUC

See also: precision, recall, ROC curve, AUC, F1 score, confusion matrix, precision-recall curve PR AUC (Precision-Recall Area Under the Curve), also referred...

Model Evaluation

Pandas

Pandas is an open-source data analysis and manipulation library for the Python programming language, providing high-performance, flexible data structures...

AI Tools & ProductsData Science

Parameter

In machine learning and statistics, a parameter is an internal variable of a model whose value is learned from data during the training process.[1] Parameters...

Neural Networks

Partial derivative

See also: Machine learning terms A partial derivative measures how a multivariable function changes when one of its inputs is varied while every other input is...

Mathematics

Participation Bias

Participation bias is a systematic error that arises when the individuals who choose to take part in a study, survey, or data collection effort differ in...

Data & DatasetsStatistics

Pass@k

Pass@k is the standard metric for evaluating code generation models: it measures the probability that at least one of k generated candidate solutions passes...

AI BenchmarksAI Code Generation

Pattern Recognition

Pattern recognition is the automatic discovery of regularities in data through the use of computer algorithms, and the use of those regularities to take...

Artificial IntelligenceComputer Science

Perceptron

A perceptron is the earliest trainable artificial neural network: a single-layer linear model that classifies inputs into two categories by computing a...

Neural Networks

Performance

See also: Machine learning terms Performance in machine learning is an overloaded word. It refers to two related but distinct ideas. The first is the quality...

Permutation variable importances

Permutation variable importance is a model-agnostic technique that measures how much a fitted machine learning model relies on a given feature by randomly...

Interpretability

Perplexity

Perplexity has two distinct meanings in the field of artificial intelligence. In natural language processing and information theory, perplexity (often...

MathematicsNatural Language Processing

Pipeline

This article is about machine learning pipelines (the end-to-end ML workflow). For splitting a model across devices by layer during distributed training, see...

MLOps

Pipelining

See also: Machine learning terms Pipelining is a term used in two distinct senses within machine learning and artificial intelligence. The first refers to the...

MLOpsTraining & Optimization

Policy

See also: Reinforcement learning, Q-learning, Markov decision process In reinforcement learning (RL), a policy is the function that maps an agent's observed...

Reinforcement Learning

Policy gradient methods

Policy gradient methods are a family of reinforcement learning algorithms that directly parameterise the agent's policy and optimise it by stochastic gradient...

Reinforcement LearningTraining & Optimization

Pose estimation

Pose estimation is the computer vision task of detecting and localizing the keypoints (also called landmarks or joints) of a human body, hand, face, animal, or...

Computer VisionDeep Learning

Positive class

In binary classification, the positive class is the class a model is testing for: the outcome it exists to detect, such as "spam," "fraud," or "tumor." The...

Post-training

Post-training is the stage of large language model (LLM) development that comes after pre-training and turns a raw, general-purpose base model into an aligned,...

Artificial IntelligenceDeep Learning

Pre-Trained Model

A pre-trained model is a machine learning model that has already been trained on a large, general-purpose dataset and can then be reused, either as a fixed...

Deep LearningNatural Language Processing