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AccueilAI GlossaireMachine LearningQu'est-ce qu'un Algorithme

AI Glossaire

0-9
3D Reconstruction2-stage detector9-layer network5G + AI0-shot learning6DoF pose estimation3D convolution8-bit quantization7D representation1-shot learning4D data
A
A/B TestingAccountabilityAccuracyAcoustic ModelingActivation FunctionsActive LearningActor-Critic MethodsActuatorsAdaDeltaAdaGradAdam OptimizerAdjusted R-SquaredAdversarial AttacksAffordance LearningAgent-Based ModelingAgentic AI / Autonomous AgentsAgentic AI FrameworksAgglomerative ClusteringAI AcceleratorsAI Act (EU)AI AgentsAI AlignmentAI and BiasAI and SustainabilityAI APIsAI Art GenerationAI AssistantsAI AuditAI AuditingAI Bill of Rights (US Blueprint)AI ContainmentAI DemocratizationAI Ethics BoardsAI Ethics GuidelinesAI Feature StoreAI for Climate ChangeAI Generated ContentAI Governance FrameworksAI GuardrailsAI HallucinationsAI in Healthcare EthicsAI in WarfareAI LegislationAI LiteracyAI MarketplacesAI Model GovernanceAI Model HubAI Model RegistryAI Model WeightsAI Music GenerationAI OrchestrationAI PolicyAI RegulationsAI SafetyAI SecurityAI SingularityAI Transparency ReportAI WatermarkingAI WinterAI Workflow AutomationAI-as-a-ServiceAlan TuringAlgorithmic AccountabilityAlgorithmic Bias MitigationAlgorithmic DiscriminationAlgorithmic TransparencyAndrew NgAnomaly DetectionAnomaly Detection in SecurityAnthropicApache KafkaAPI DevelopmentAPI EndpointsApriori AlgorithmArtificial General Intelligence (AGI)Artificial Neural NetworksArtificial SuperintelligenceASICsAssociation Rule LearningAsynchronous Advantage Actor-CriticAttention MechanismsAUCAudio ClassificationAudio Signal ProcessingAugmented RealityAuthenticationAuthorizationAutoencodersAutomated ReasoningAutomatic Speech Recognition (ASR)AutomationAutoMLAutonomous NavigationAutoregressive ModelsAlgorithmAutoencoderAGI / Artificial General IntelligenceAttentionArtificial Intelligence (AI)
B
Bag-of-Words ModelBaggingBatch SizeBayesian InferenceBayesian NetworksBayesian OptimizationBias in AIBias-Variance TradeoffBig DataBig Data TechnologiesBiometric SecurityBLEU ScoreBlockchain in AIBox PlotByte-Pair Encoding (BPE)BERTBatch NormalizationBackpropagationBiasBoosting
C
CaffeCalculusCalibrationCalifornia Consumer Privacy Act (CCPA)Canary DeploymentCapsule NetworksCarbon Footprint of AICase-Based ReasoningCatastrophic ForgettingCentral Limit TheoremChain-of-ThoughtChinese Room ArgumentClass ImbalanceClassificationCloud AI PlatformsCloud ComputingClustering AlgorithmsCode Generation ModelsCognitive ArchitecturesCognitive ComputingCohereColab NotebooksCollaborative FilteringColor SpacesComplex AnalysisComplianceCompliance Standards (ISO IEEE)Computational ComplexityComputational Fluid DynamicsComputational Theory of MindCompute-Optimal ModelsConcept DriftConceptual GraphsConditional ProbabilityConfusion MatrixConsciousness in AIConsistency ModelsConstitutional AIConstraint Satisfaction ProblemsContainerizationContent-Based FilteringContext WindowContinual LearningContinuous Integration/Continuous Deployment (CI/CD)Control SystemsConversational AIConvolutional Neural NetworksCOPPACoreference ResolutionCorrelationCorrelation MatrixCost-Sensitive LearningCross-Entropy LossCurriculum LearningCyber Threat IntelligenceCybersecurity RegulationsChatbotClassifier / ClassificationCNN / Convolutional Neural NetworkCross-ValidationClustering
D
DALL·EData AnnotationData CatalogData CentersData CleaningData DriftData GovernanceData IngestionData IntegrationData LabelingData LakeData LakesData LeakageData LineageData MiningData PipelineData PoisoningData PreprocessingData PrivacyData ProtectionData Protection LawsData QualityData SecurityData SovereigntyData TransformationData VersioningData VisualizationData Visualization TechniquesData WarehousingDatabases for AIDavies-Bouldin IndexDBSCANDecision Boundary VisualizationDecision TreesDeep Belief NetworksDeep Q-NetworksDeep Reinforcement LearningDeepfakesDeepMindDemis HassabisDependency ParsingDepth EstimationDescriptive StatisticsDialogue SystemsDifferential EquationsDifferential EvolutionDifferential PrivacyDiffusion ModelsDigital DivideDigital ProvenanceDigital TwinsDimensionality ReductionDirect Preference Optimization (DPO)Discourse AnalysisDiscrete Event SimulationDiscrete MathematicsDisinformationDistributed ComputingDistributed File SystemsDistributed TrainingDockerDronesDropoutDropout RegularizationDynamical SystemsDeterministic ModelDiscriminative ModelDeepfakeData AugmentationDeep Learning
E
Early StoppingEdge AIEdge ComputingEdge DetectionEigenvalues and EigenvectorsElon MuskEmbedding SizeEmbeddingsEmbodied AIEmergent AbilitiesEmotion RecognitionEnsemble MethodsEpisodic MemoryEthical AIEthical AI GuidelinesEthical AuditingEthical Decision-MakingEthical DilemmasEthical FrameworksEthics of AIETL ProcessesEvolutionary AlgorithmsExistential RiskExpectation-MaximizationExpectation-Maximization AlgorithmExpected Calibration ErrorExpert SystemsExplainabilityExploration vs. ExploitationExploratory Data AnalysisExport ControlsEpochEncoderEnsemble LearningEmbeddingExplainable AI (XAI)
F
F1 ScoreFacial RecognitionFairnessFastAIFeature EngineeringFeature ImportanceFeature SelectionFeature StoreFeature StoresFederated LearningFei-Fei LiFew-Shot LearningFinite Element AnalysisFirst-Order LogicFlow MatchingForce ControlFoundation Model EconomyFoundation ModelsFourier TransformFPGAsFrame LanguagesFunctional AnalysisFoundation ModelFeature ExtractionFusion / Multimodal FusionForward PropagationFine-tuning
G
Game Playing AIGame TheoryGame Theory SimulationsGated Recurrent UnitsGaussian Mixture ModelsGeneral Data Protection Regulation (GDPR)Generative Adversarial NetworksGenerative ModelsGenetic AlgorithmsGensimGeoffrey HintonGlobal CooperationGPT ModelsGrad-CAMGradient Boosting MachinesGradient ClippingGraph Neural NetworksGraph TheoryGraphics Processing Units (GPUs)Grid SearchGAN / Generative Adversarial NetworkGenerative AIGradient DescentGroundingGraph Neural Network (GNN)
H
HadoopHeatmapHelpHeuristic AlgorithmsHidden Markov ModelsHierarchical Reinforcement LearningHigh-Performance ComputingHIPAAHistogramHOGHPC ClustersHugging FaceHugging Face TransformersHuman RightsHuman-in-the-LoopHuman-Robot InteractionHyperparameter OptimizationHyperparameter TuningHyperparameterHidden LayerHallucinationHeuristicHierarchical Model
I
Ilya SutskeverImage CaptioningImage ClassificationImage RecognitionImage SegmentationImpact on EmploymentIn-Context LearningIndustrial RobotsInferenceInference EnginesInference OptimizationInferential StatisticsInformation TheoryInformed ConsentInfrastructure as CodeInstance SegmentationIntellectual Property RightsIntelligent AgentsIntrusion Detection SystemsInverse Reinforcement LearningInstance / SampleInstruction tuningIntelligence Amplification / AugmentationInterpretabilityImbalanced Data
J
John McCarthyJoint Probability DistributionJuergen SchmidhuberJupyter NotebooksJAXJoint EmbeddingJSONL / JSON-linesJuxtapositionJittering
K
K-Nearest NeighborsKai-Fu LeeKalman FiltersKerasKnowledge CutoffKnowledge GraphsKnowledge RepresentationKubernetesKnowledge DistillationKL Divergence (Kullback–Leibler Divergence)K-means ClusteringK-Shot LearningKernel Trick
L
L1 RegularizationL2 RegularizationLabel SmoothingLanguage ModelingLanguage ModelsLaplace TransformLarge Language Models (LLMs)Large Multimodal ModelsLatent Dirichlet AllocationLatent SpaceLaw of Large NumbersLayer NormalizationLearning CurveLearning Rate DecayLearning Rate SchedulingLemmatizationLIMELinear AlgebraLinear RegressionLog LossLogic ProgrammingLogistic RegressionLong Short-Term Memory NetworksLong-Context ModelsLoRA (Low-Rank Adaptation)Large Language Model (LLM)Latent VariableLoss FunctionLearning RateLSTM / Long Short-Term Memory
M
Machine ConsciousnessMachine TranslationMarkov Chain ModelsMarkov Chain Monte CarloMarkov Decision ProcessesMarkov ModelsMarvin MinskyMasked Language ModelsMaster Data ManagementMatplotlibMatrix DecompositionMCPMean Absolute ErrorMean Squared ErrorMechanistic InterpretabilityMel-Frequency Cepstral Coefficients (MFCCs)Metadata ManagementMicroservicesMidjourneyMind UploadingMini ToolMini-Batch Gradient DescentMixture of Experts (MoE)MLOpsMobile RobotsModel CardsModel CompressionModel DeploymentModel DriftModel Explainability ToolsModel MonitoringModel ServingModel StealingMomentum OptimizationMonitoring and LoggingMonte Carlo MethodsMonte Carlo SimulationsMoral MachinesMotion DetectionMotion PlanningMulti-Armed Bandit ProblemMultimodal AIMusic Information RetrievalMXNetModelMachine Learning (ML)Meta-learningMulti-head AttentionMultimodal / Multimodality
N
n-GramsNaive Bayes AlgorithmNaive Bayes ClassifierNamed Entity RecognitionNatural Language Generation (NLG)Natural Language ProcessingNatural Language Processing (NLP)Natural Language UnderstandingNesterov Accelerated GradientNetwork SimulationsNeural Architecture SearchNeural NetworksNeural Processing Unit (NPU)Neuromorphic ComputingNick BostromNLTKNoise ReductionNoSQL DatabasesNumPyNVIDIA CUDANeural NetworkNovelty Detection / Anomaly DetectionNLP / Natural Language ProcessingNLU / Natural Language UnderstandingNormalization
O
Object DetectionObject TrackingOntologiesOpenAIOpenAI GPTOptical Character RecognitionOptimization TheoryOut-of-Distribution (OOD) DataOptimizerOnline LearningObjective FunctionOverfittingOne-hot Encoding
P
PandasParallel ComputingParameter CountParameter-Efficient Fine-Tuning (PEFT)Part-of-Speech TaggingPartial Dependence PlotsPath PlanningPattern RecognitionPeople also viewedPerception in AIPerceptronPerplexityPeter NorvigPhilosophy of MindPhoneticsPipelinesPlanning and SchedulingPlotlyPolicy GradientsPolicy OptimizationPose EstimationPositional EncodingPragmaticsPrecisionPredictive ModelingPredictive ProbabilityPreference TuningPrincipal Component AnalysisPrivacyPrivacy-Preserving Machine LearningProbability Density FunctionsProbability TheoryProblem SolvingProcess ModelingProcess-Based SupervisionPrompt ChainingPrompt EngineeringPrompt InjectionPrompt MarketplacePrompt TemplatesPropositional LogicProximal Policy OptimizationPruningPyTorchParameterPromptPolicy / Reinforcement Learning PolicyPoolingPretraining
Q
QLoRA (Quantized Low-Rank Adaptation)QueryQuality EstimationQuantizationQ-learningQueue / BufferQuantum ComputingQuantum Machine LearningQuestion AnsweringQuestion Answering Systems
R
Representation LearningReinforcement Learning (RL)Retrieval Augmented Generation (RAG)RegularizationRNN / Recurrent Neural NetworkR-SquaredRandom ForestsRandom SearchRay KurzweilReal AnalysisReasoning EnginesRecallRecommender SystemsRecurrent Neural NetworksRed TeamingRegressionRegression AnalysisRegulatory ComplianceReinforcement Learning from Human FeedbackReinforcement Learning in RoboticsReproducibilityResponsible AIRetrieval-Augmented GenerationReward FunctionRMSpropRobot KinematicsRobot VisionRobotic ManipulationRobotic Operating System (ROS)Robotics TransformersRobustness in AI ModelsROC CurveRodney BrooksRoot Mean Squared ErrorRule-Based Systems
S
Self-Supervised LearningSupervised LearningSamplingSequence ModelingSoftmaxSaliency MapsSARSA AlgorithmScalable OversightScaling LawsScatter PlotScikit-LearnSciPySeabornSearch AlgorithmsSecure HardwareSecure Multi-Party ComputationSecure ProtocolsSelf-AttentionSelf-Driving CarsSemantic NetworksSemantic ParsingSemantic Role LabelingSemantic SegmentationSemantic WebSemi-Supervised LearningSensorsSentencePieceSentiment AnalysisSequence LabelingServerless ComputingServerless GPUsSet TheorySHAP ValuesSiamese NetworksSIFTSilhouette ScoreSimulated AnnealingSimulation HypothesisSimulation-to-Real Transfer (Sim2Real)Simultaneous Localization and Mapping (SLAM)SMOTESocial Acceptance of AISocial SimulationSOTA (State of the Art)spaCySparkSpeaker DiarizationSpectrogram AnalysisSpeech EnhancementSpeech RecognitionSpeech SynthesisSpiking Neural NetworksSQLStable DiffusionStackingState-Action PairsStatistical AnalysisStatistical DistributionsStatisticsStemmingStochastic Gradient DescentStochastic ModelingStochastic ProcessesStop WordsStream ProcessingStrong AIStrong vs. Weak AIStuart RussellStyle TransferSubword TokenizationSupport Vector MachinesSURFSurveillanceSwarm IntelligenceSymbolic AISynthetic Data GenerationSynthetic MediaSystem DynamicsSystem Prompt
T
TokenizerTransfer LearningTransformerTuning / Hyperparameter TuningTraining Datat-SNETeacher ForcingTechnological SingularityTeleoperationTemperatureTemporal Difference LearningTensor Processing Units (TPUs)TensorFlowTesting and ValidationText SummarizationText-to-Audio GenerationText-to-Image GenerationText-to-Speech (TTS)Text-to-Video GenerationTF-IDFTheanoTime Series AnalysisTimnit GebruTinyMLToken LimitTokenizationTokensTool Use (LLMs)Topic ModelingTopologyTransformer ModelsTransformer NetworksTransparencyTransparency RequirementsTrust Region Policy OptimizationTrustworthy AITruthfulness (in LLMs)Turing Test
U
Unsupervised LearningUncertainty EstimationUnderfittingUniversal Approximation TheoremU-NetUMAPUnmanned Aerial Vehicles (UAVs)Unmanned Ground Vehicles
V
Variational Autoencoder (VAE)Vector EmbeddingValidation SetVanishing / Exploding GradientVision Transformer (ViT)Validation CurveValue FunctionVector DatabaseVersion Control for ModelsVibe code an AI ToolVideo Generation ModelsVirtual Reality SimulationsVoice BiometricsVoice CloningVoice Conversion
W
Weak SupervisionWeight DecayWhitening / Whitening TransformationWord EmbeddingWorkflowWarmup StepsWeak AIWord EmbeddingsWord Sense DisambiguationWordPieceWorld Models
X
X-axis / feature axisXOR problemXAI / Explainable AIXLMXLNet
Y
Y-axis / feature axisYield (model yield / throughput)Yoga of AIY-transform / YUVYAGNI (You Aren't Gonna Need It)Yann LeCunYoshua Bengio
Z
Zero-shot Learning / Zero-shot inferenceZero-centric / Zero-bias initializationZ-score NormalizationZygosity in augmentationZero-gradient phenomenonZero Trust Architecture

Qu'est-ce qu'un Algorithme

Machine Learning
[wˌʌt ɪz ˈælɡɚɹˌɪθəm]
Dernière mise à jour: 15 octobre 2025

Un algorithme est un ensemble de règles et d'étapes utilisées pour résoudre des problèmes ou exécuter des tâches. Il trouve son origine dans le mot grec et joue un rôle crucial en informatique. Des algorithmes de tri et de recherche basiques aux modèles complexes d'apprentissage automatique, les algorithmes sont appliqués dans divers domaines tels que la finance, la santé et le transport.


Le fonctionnement d'un algorithme se compose généralement de trois parties : l'entrée, le traitement et la sortie. Par exemple, un algorithme de tri prend un tableau non trié comme entrée, le traite et produit un tableau trié en sortie.


Les algorithmes sont largement utilisés dans les moteurs de recherche, les recommandations sur les réseaux sociaux, l'analyse de données et l'intelligence artificielle. Par exemple, le moteur de recherche Google utilise des algorithmes complexes pour déterminer quelles pages Web sont les plus pertinentes pour les requêtes des utilisateurs, fournissant ainsi les meilleurs résultats de recherche.


À mesure que la technologie progresse, la recherche et l'application des algorithmes continueront d'évoluer, en particulier dans les domaines de l'intelligence artificielle et des big data. Les algorithmes deviendront plus complexes et intelligents, capables de traiter des ensembles de données plus importants et de fournir des résultats plus précis.


Bien que les algorithmes offrent des avantages tels que l'efficacité, la répétabilité et la précision, ils présentent également des limites, telles que la dépendance à la qualité des données et les biais potentiels. Il est donc crucial d'évaluer soigneusement leur applicabilité et leurs risques lors de l'utilisation des algorithmes.


Lors de la conception et de l'utilisation d'algorithmes, il est essentiel de garantir la transparence et l'interprétabilité. Cela aide les utilisateurs à comprendre le processus décisionnel de l'algorithme, augmentant ainsi la confiance dans les résultats de l'algorithme.

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