Machine Learning

F1 score

The F1 score (also written as F1-score, F-score, or F-measure) is the harmonic mean of precision and recall, calculated as , and it ranges from 0 (worst) to 1...

Model EvaluationStatistics

Fairness Constraint

A fairness constraint is an explicit mathematical condition imposed on a machine learning model during training, evaluation, or post-processing that forces its...

AI Ethics

Fairness Metric

A fairness metric is a quantitative, mathematical measure used to evaluate whether a machine learning model's predictions or decisions treat different...

AI EthicsModel Evaluation

False Negative Rate

The false negative rate (FNR), also known as the miss rate, is the proportion of actual positive instances that a model or test incorrectly classifies as...

Model EvaluationStatistics

False Positive Rate (FPR)

The false positive rate (FPR) is the proportion of actual negative cases that a test, model, or decision process incorrectly classifies as positive, defined as...

Model EvaluationStatistics

False negative

A false negative (FN), also called a Type II error or a miss, is an instance whose true label is positive but that a classification model or test predicts as...

Model EvaluationStatistics

False positive

A false positive (FP), also called a Type I error or a false alarm, is an instance whose true label is negative but whose predicted label is positive: the...

Model EvaluationStatistics

Feature

In machine learning and statistics, a feature is an individual measurable property or characteristic of a phenomenon being observed, used as an input variable...

Data & Datasets

Feature Cross

A feature cross (also called a crossed feature or feature interaction) is a synthetic feature created by combining two or more existing features so that a...

Data & Datasets

Feature Engineering

Feature engineering is the process of using domain knowledge to create, transform, and select features from raw data so that machine learning models can learn...

Data & Datasets

Feature Extraction

Feature extraction is the process of transforming raw data into a smaller set of derived, informative numerical variables called features that capture the...

Data & Datasets

Feature Importances

Feature importances are numeric scores that quantify how much each input feature contributes to the predictions of a machine learning model. The three dominant...

InterpretabilityModel Evaluation

Feature Selection

Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine...

AlgorithmsData & Datasets

Feature Set

A feature set is the complete collection of input variables (features, attributes, or predictors) that a machine learning model uses to learn patterns and make...

Data & DatasetsData Science

Feature Vector

A feature vector is an n-dimensional, ordered list of numerical values that represents the measurable properties of an object, data point, or observation in a...

Data & Datasets

Feature spec

See also: Machine learning terms A feature spec (short for feature specification) is a declarative description of the input features used by a machine learning...

Federated Learning

Federated learning is a machine learning technique that trains a shared model across many decentralized devices or servers without moving their raw data to a...

AI EthicsDeep Learning

Feedback Loop

A feedback loop in machine learning is a cycle in which a deployed model's predictions influence the real world, and the resulting data is then collected and...

AI Ethics

Feedforward Neural Network (FFN)

A feedforward neural network (FFN), also called a multilayer perceptron (MLP) when it has multiple layers, is a type of artificial neural network in which...

Deep LearningNeural Networks

Few-Shot Learning

Few-shot learning is a branch of machine learning in which a model learns to recognize new classes or perform new tasks from only a very small number of...

Deep Learning

Fine Tuning

Fine-tuning is a machine learning technique that takes a pre-trained model and further trains it on a smaller, task-specific dataset, adjusting the model's...

Deep LearningTraining & Optimization

FineWeb

FineWeb is a large-scale, open pretraining dataset for large language models (LLMs) created by Hugging Face. Released in April 2024, it contains approximately...

Data & DatasetsNatural Language Processing

FineWeb-2

FineWeb-2 (also written FineWeb2) is a massively multilingual web pretraining dataset released by Hugging Face in December 2024. It is the multilingual...

Data & Datasets

FineWeb-Edu

FineWeb-Edu is an open, English-language pretraining dataset of roughly 1.3 trillion tokens, built by filtering the much larger FineWeb web corpus down to the...

Data & DatasetsLarge Language Models

Flash Attention

Flash Attention is a family of IO-aware, exact attention algorithms that accelerate and shrink the memory footprint of the attention mechanism in transformer...

Focal loss

Focal loss is a loss function that reshapes standard cross-entropy loss by adding a (1 - pt)^gamma modulating factor, which down-weights well-classified (easy)...

Computer VisionDeep Learning

Forget Gate

The forget gate is a sigmoid layer inside a Long Short-Term Memory (LSTM) recurrent neural network that decides, element by element, how much of the previous...

Neural Networks

Full Softmax

Full softmax (also called the standard softmax or exact softmax) is the softmax computation that calculates a probability for every possible output class in a...

Deep LearningNatural Language Processing

Fully Connected Layer

A fully connected layer (also called a dense layer or linear layer) is a layer in an artificial neural network in which every input value connects to every...

Deep LearningNeural Networks

GGML

GGML is an open-source tensor library written in pure C that runs machine learning inference efficiently on consumer hardware, and it is the computational...

Developer ToolsOpen Source AI

GGUF

GGUF (GPT-Generated Unified Format) is the standard binary file format for storing large language models for local inference, bundling a model's weights,...

Developer ToolsLarge Language Models

GLUE benchmark

The General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding (NLU) tasks designed to evaluate and...

AI BenchmarksNatural Language Processing

GPU computing

GPU computing is the use of a graphics processing unit (GPU) to perform general-purpose computation that was traditionally handled by the central processing...

AI HardwareAI Infrastructure

GSM8K

GSM8K (Grade School Math 8K) is a benchmark dataset of 8,792 grade-school-level math word problems created by researchers at OpenAI to evaluate the multi-step...

AI BenchmarksLarge Language Models

GaLore (Gradient Low-Rank Projection)

GaLore (Gradient Low-Rank Projection) is a memory-efficient training strategy for large neural networks that projects each weight matrix's gradient into a...

Training & Optimization

Gated SAE

A Gated sparse autoencoder (Gated SAE) is a sparse-autoencoder architecture for mechanistic interpretability that splits the encoder into a gating path, which...

AI Safety

Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probabilistic model that represents a dataset as a weighted mixture of a finite number of Gaussian distributions with...

Gaussian Process

A Gaussian process (GP) is a probabilistic machine learning model defined as a collection of random variables, any finite number of which have a joint Gaussian...

Statistics

Gemma Scope

Gemma Scope is an open, comprehensive suite of sparse autoencoders (SAEs) released by Google DeepMind in 2024 to support mechanistic interpretability research...

AI Safety

Generalization

See also: Machine learning terms, Bias-variance tradeoff Generalization in machine learning is the ability of a trained model to perform accurately on new,...

Deep LearningModel Evaluation

Generalization Curve

A generalization curve (also called a learning curve) is a plot that visualizes how a machine learning model's performance on training data and unseen data...

Model Evaluation

Generalized Linear Model

A generalized linear model (GLM) is a flexible extension of ordinary linear regression that allows the response variable to follow any distribution from the...

Statistics

Generative AI

Generative AI is a category of artificial intelligence that creates new content, such as text, images, audio, video, code, and 3D models, by learning the...

Artificial IntelligenceGenerative AI

Generative Model

See also: Machine learning terms, Discriminative model A generative model is a class of statistical and machine learning model that learns the joint...

Deep LearningGenerative AI

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning model in which two neural networks, a generator and a discriminator, are trained...

Deep LearningGenerative AI

Generator

A generator is a neural network within a generative adversarial network (GAN) that learns to produce synthetic data samples from random noise. It maps a point...

Generative AINeural Networks

Geoffrey Hinton

Geoffrey Hinton is a British-Canadian computer scientist and cognitive psychologist, widely called the "Godfather of AI," who won the 2024 Nobel Prize in...

Artificial Intelligence

Gini Impurity

Gini impurity is the probability that a randomly chosen element from a dataset would be incorrectly classified if it were labeled at random according to the...

Google DeepMind

Google DeepMind is Alphabet's central artificial intelligence research and product organization, formed on April 20, 2023 by merging the DeepMind lab (founded...

Artificial IntelligenceGoogle

Grad-CAM

Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for producing visual explanations from convolutional neural network (CNN) models by using...

Computer VisionDeep Learning

Gradient

In machine learning, the gradient is the vector of partial derivatives of a loss function with respect to every model parameter, and it points in the direction...

MathematicsTraining & Optimization

Gradient Accumulation

Gradient accumulation is a deep learning training technique that simulates a large batch size on limited GPU memory by summing the gradients from several small...

Deep LearningTraining & Optimization

Gradient Boosting

See also: Machine learning terms Gradient boosting is an ensemble machine learning technique that builds a predictive model by combining many weak learners,...

Gradient Descent

Gradient descent is a first-order iterative optimization algorithm that minimizes a differentiable loss function by repeatedly stepping in the direction of the...

Deep LearningTraining & Optimization

Gradient boosted (decision) trees (GBT)

See also: Machine learning terms, Gradient boosting Gradient boosted decision trees (GBT, also written GBDT, GBM, or GBRT) is an ensemble method that builds a...

Gradio

Gradio is an open-source Python library that lets developers build interactive web interfaces for machine learning models, APIs, and arbitrary Python functions...

Developer ToolsOpen Source AI

Graph

In machine learning the word graph has two unrelated meanings. The first is a graph as a data structure: a set of nodes (vertices) connected by edges, written...

Graph Execution

Graph execution is a computation paradigm in machine learning frameworks where mathematical operations are organized into a directed acyclic graph (DAG) before...

Deep LearningSoftware Development

Graph Machine Learning Models

Graph machine learning models are neural networks designed to operate on data structured as graphs, where the input is a set of nodes connected by edges rather...

AI ModelsModel Architecture

Greedy Policy

In reinforcement learning, a greedy policy is a decision rule that, in every state, selects the action with the highest estimated value, formally the action...

Reinforcement Learning