Leaf
A leaf (also called a terminal node) is a node in a decision tree that has no children and holds the model's prediction.
Machine Learning
Explore learning methods, model architectures, datasets, and practical applications.
Explore articles
Browse subtopics (65)
Articles that also belong to these categories. Counts cover all of Machine Learning.
All articles (873)Deep Learning (188)Natural Language Processing (92)Statistics (90)Training & Optimization (70)Data & Datasets (65)Neural Networks (58)Reinforcement Learning (58)Model Evaluation (56)Artificial Intelligence (54)Large Language Models (52)Mathematics (48)Computer Vision (42)AI Ethics (35)AI Infrastructure (29)AI Safety (26)Open Source AI (26)Data Science (24)AI Benchmarks (22)Developer Tools (22)Algorithms (20)AI Hardware (19)AI Tools & Products (17)Google (16)Information Retrieval (16)AI Inference (15)Model Architecture (15)Generative AI (14)People (14)Robotics (14)AI Research (13)AI Agents (12)Interpretability (12)MLOps (11)Transformer Models (11)AI Companies (9)AI Models (9)AI Compute (8)Computer Science (8)AI Code Generation (7)Multimodal AI (7)Speech & Audio AI (7)AI for Science (6)Chinese AI (6)AI Policy & Regulation (5)Meta AI (5)Programming Languages (5)AI History (4)Embodied AI (4)Prompt Engineering (4)AI Alignment (3)AI Events (3)Drug Discovery (3)Healthcare AI (3)Reasoning Models (3)Enterprise AI (2)Software Development (2)World Models (2)2025 in artificial intelligence (1)Anthropic (1)Diffusion Models (1)Education AI (1)Image Generation (1)NVIDIA (1)Research Organizations (1)Small Language Models (1)
Showing 421-480 of 873 articles
Learning Rate
The learning rate is a hyperparameter that scales an update made by an iterative optimization algorithm. In machine learning, it is commonly written as η or α.
Least Squares Regression
Least squares regression is a statistical method that fits a model to data by choosing the parameters that minimize the sum of the squared residuals, the squared differences between the observed values and the…
Leo Breiman
Leo Breiman (January 27, 1928 to July 5, 2005) was an American statistician at UC Berkeley whose work on tree-based prediction supplied machine learning with several of its most durable tools.
LightGBM
LightGBM (short for Light Gradient-Boosting Machine) is a free and open-source gradient boosting framework that trains ensembles of decision trees on tabular data, originally developed at Microsoft Research by…
Lilian Weng
Lilian Weng is an artificial intelligence researcher known for her work at OpenAI, where she spent about seven years and led the Safety Systems team as Vice President of Research and Safety, and for her…
Linear
In machine learning and mathematics, linear describes a function or relationship in which the output is built from the inputs only through addition and multiplication by constants.
Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a classical statistical method for classification and dimensionality reduction that finds the linear combination of features which best separates two or more classes by…
Linear Regression
Linear regression is a statistical method that models the relationship between one or more independent variables (the predictors or features) and a continuous dependent variable (the response) by fitting a…
Linear model
A linear model is any statistics or machine learning model whose prediction is a linear function of its input features
LiveBench
LiveBench is a contamination-limited benchmark for large language models (LLMs) that draws frequently updated questions from recent sources (math competitions, arXiv papers, news articles, and datasets) and…
LiveCodeBench
LiveCodeBench is a holistic and contamination-free benchmark for evaluating large language models on code, first released in March 2024 by researchers at UC Berkeley, MIT, and Cornell led by Naman Jain.
Llama 2
Llama 2 is a family of open-weight large language models developed by Meta AI. Meta released pretrained and dialogue-tuned checkpoints with 7 billion, 13 billion, and 70 billion parameters on July 18, 2023.
LoRA (Low-Rank Adaptation)
Low-Rank Adaptation, usually abbreviated LoRA, is a parameter-efficient fine-tuning method for adapting a pre-trained model.
Log Loss
Log loss is the negative log-likelihood of the predicted probabilities and the standard loss function for probabilistic classification: for binary labels it is computed as…
Log-Odds
Log-odds, also known as the logit, is a mathematical transformation that converts a probability value between 0 and 1 into a real number spanning from negative infinity to positive infinity.
Logistic Regression
Logistic regression is a statistical model for a binary response. It represents the conditional probability of one outcome as the logistic transformation of a linear predictor.
Logits
In machine learning and statistics, logits refer to the raw, unnormalized scores output by a model before they are converted into probabilities.
Loss
In machine learning, loss (sometimes called error) is a single non-negative number that measures how far a model's prediction is from the correct answer
Loss Curve
A loss curve is a plot that shows the value of a loss function over the course of training a machine learning model.
Loss Function
A loss function assigns a numerical cost to a prediction, decision, or action after an outcome is observed.
Loss Surface
The loss surface (also called the loss landscape, the error surface, or the objective function surface) is the geometric representation of a loss function as a function of the model's parameters.
Low-Resource Languages
Low-resource languages, in natural language processing (NLP), are languages with little of the raw material that modern language technology depends on: digitized text, parallel translation corpora, annotated…
MBPP
MBPP (Mostly Basic Python Problems) is a code generation benchmark of 974 crowd-sourced Python programming tasks designed to be solvable by entry-level programmers, introduced by Jacob Austin, Augustus Odena…
METEOR (metric)
METEOR (Metric for Evaluation of Translation with Explicit ORdering) is an automatic evaluation metric for machine translation and other text-generation tasks that scores a candidate sentence against one or…
MLPerf
MLPerf is the AI industry's standard suite of benchmarks for measuring how fast machine learning systems train and run models, developed and maintained by MLCommons
MLflow
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, covering experiment tracking, model packaging, a model registry, deployment, and (since 2025) generative-AI…
MMLU-Pro
MMLU-Pro (Massive Multitask Language Understanding Professional) is an artificial intelligence benchmark of 12,032 ten-choice questions across 14 academic domains
MMMU
MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark) is a multimodal AI benchmark of 11,550 college-level questions that pairs text with images to test expert knowledge and…
MNIST
The Modified National Institute of Standards and Technology (MNIST) database is a collection of 70,000 grayscale images of handwritten digits (0 through 9) that has served as one of the most widely used…
MXFP4
MXFP4 (Microscaling FP4) is a 4-bit block-scaled number format for quantization in machine learning, defined in the Open Compute Project (OCP) Microscaling Formats (MX) Specification v1.0 of September 2023.
Machine Learning
Machine learning (ML) is a branch of artificial intelligence that gives computers the ability to learn from data and improve their performance on tasks without being explicitly programmed.
Machine Translation
Machine translation (MT) is the automatic translation of text or speech from one human language into another.
Machine learning terms
Machine learning terms are the vocabulary used to describe data, models, training procedures, evaluation methods, and deployed behavior.
Machine learning terms/All
This alphabetical glossary collects core terminology used across machine learning, deep learning, reinforcement learning, large language models, and TensorFlow tooling.
Machine learning terms/Clustering
Clustering is a class of unsupervised machine learning techniques that partition a set of objects into groups, called clusters
Machine learning terms/Computer Vision
The key machine learning terms for computer vision describe how neural networks turn pixels into predictions: a convolution slides a small learned filter (kernel) across an image to detect patterns, a…
Machine learning terms/Decision Forests
Decision forests are a family of machine learning models built from collections of decision trees.
Machine learning terms/Fairness
The key machine learning fairness terms are the formal criteria used to define and measure when a model treats demographic groups equitably, together with the named biases that make models unfair.
Machine learning terms/Fundamentals
Machine learning (ML) is the branch of artificial intelligence concerned with building systems that learn patterns from data rather than following explicitly programmed rules.
Machine learning terms/Google Cloud
Google Cloud is the public cloud arm of Google and one of the three dominant providers of machine learning infrastructure, alongside Amazon Web Services and Microsoft Azure.
Machine learning terms/Natural Language Processing
Natural Language Processing (NLP) is the subfield of artificial intelligence and machine learning concerned with enabling computers to read, interpret, generate, and reason about human language in text and…
Machine learning terms/Recommendation Systems
A recommendation system (also called a recommender system) is a class of machine learning software that predicts the preference a user would give to an item
Machine learning terms/Reinforcement Learning
Reinforcement learning (RL) is a branch of machine learning in which an agent learns to make sequential decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Machine learning terms/Sequence Models
Sequence models are a class of machine learning systems designed to process inputs or produce outputs that have a meaningful order.
Machine learning terms/TensorFlow
TensorFlow is an open-source software library for machine learning, deep learning, and numerical computation, developed and maintained by Google and first released to the public under the Apache 2.0 license on…
Majority class
In machine learning, the majority class is the class label that appears most frequently in a labeled dataset used for classification.
Mamba
Mamba is a neural network architecture for sequence modeling that uses selective state space models (SSMs) to process sequential data in linear time with respect to sequence length
Manifold Hypothesis
The manifold hypothesis is the conjecture that real-world high-dimensional data, such as natural images, speech, and text representations, concentrates on or near a manifold of much lower dimension embedded…
Manifold Learning
Manifold learning is a class of nonlinear dimensionality reduction techniques in machine learning and statistics that recover a low-dimensional structure assumed to lie within high-dimensional data, producing…
Margaret Mitchell (computer scientist)
Margaret Mitchell is an American computer scientist who works on AI ethics, fairness in machine learning, and the documentation of AI systems.
Markov Decision Process (MDP)
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making in stochastic environments.
Masked Language Model
A masked language model (MLM) is a language model trained to predict missing tokens that have been hidden in a sequence of text, using context from both the left and the right of each blank.
Masked autoencoder (MAE)
Masked autoencoder (MAE) is a self-supervised learning method for vision transformers that masks roughly 75% of an input image's patches and trains a network to reconstruct the missing pixels from the small…
Matplotlib
Matplotlib is the foundational open-source data visualization library for Python, created by John D. Hunter in 2003, that produces static, animated, and interactive plots and serves as the de-facto plotting…
Matrix factorization
Matrix factorization is a family of mathematical techniques that decompose a matrix into a product of two or more smaller matrices.
Matryoshka representation learning
Matryoshka Representation Learning (MRL) is a representation learning technique that trains a single neural model to produce embedding vectors which remain useful when truncated to many smaller dimensionalities
Maximum likelihood estimation (MLE)
Maximum likelihood estimation (MLE) is the method of choosing the parameters of a probability model so that they make the observed data as probable as possible: given a parametric model with density (or mass)…
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a regression accuracy metric and loss function that measures the average absolute difference between predicted values and actual observed values
Mean Squared Error (MSE)
Mean Squared Error (MSE), also called mean squared deviation (MSD), is the average of the squared differences between predicted values and actual (observed) values.