Linear model
A linear model is any statistics or machine learning model whose prediction is a linear function of its input features, of the form f(x) = g(w_1 x_1 + w_2 x_2 + ... + w_p x_p + b)
Machine Learning
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Showing 421-480 of 858 articles
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…
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.
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, formally defined by the 5-tuple $$(S, A, P, R, \gamma)$$: a set of states, a set…
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
MemGPT
MemGPT (short for Memory-GPT) is a system and agent design pattern that gives large language model agents long-term memory by managing data between the model's bounded context window and external storage
Membership Inference Attack
A Membership Inference Attack (MIA) is a privacy attack against a trained machine learning model in which an adversary, given a candidate data record and access to the model
Meta Prompting
Meta prompting (also spelled meta-prompting) is an advanced prompt engineering technique where large language models (LLMs) are used to generate, refine, critique, select, or optimize prompts for themselves or…
Meta-Learning
Meta-learning, often called "learning to learn", is a branch of machine learning in which a model is trained across many different tasks so that it can master a new task from only a handful of examples or a…
Metric
In machine learning, a metric is a quantitative measure used to evaluate how well a model or algorithm performs a task.
Metrics API (tf.metrics)
The Metrics API in TensorFlow is a collection of classes and utilities for computing evaluation scores that summarize how well a model is doing.
Microscaling formats
Microscaling (MX) formats are a family of low-precision number formats for machine learning in which a small block of values, normally 32 of them, shares one common scale factor while each value is stored in a…
Mini-batch
A mini-batch is a small, randomly sampled subset of the training dataset used to compute a single update to a model's parameters during training.
Minimax Loss
Minimax loss is a loss function rooted in game theory and decision theory that measures the worst-case performance of a strategy, algorithm, or model.
Minority class
In an imbalanced classification problem, the minority class is the class label with far fewer training examples than the others, and it is almost always the outcome you actually care about (fraud, disease…
Mixed-Precision Training
Mixed-precision training is a technique for training deep learning models using lower-precision floating-point formats for most computations while maintaining a higher-precision copy of the model weights for…