Glossary
LLM Collection
Datasets
Fundamentals
AblationAccuracy in Machine LearningActive Learning (Machine Learning)Adversarial Machine LearningAffective AIAI AgentsAI and EducationAI and FinanceAI and MedicineAI AssistantsAI DetectionAI EthicsAI Generated MusicAI HallucinationsAI HardwareAI in Customer ServiceAI InterpretabilityAI Lifecycle ManagementAI LiteracyAI MonitoringAI OversightAI PrivacyAI PrototypingAI Recommendation AlgorithmsAI RegulationAI ResilienceAI RobustnessAI SafetyAI ScalabilityAI SimulationAI StandardsAI SteeringAI TransparencyAI Video GenerationAI Voice TransferApproximate Dynamic ProgrammingArtificial Super IntelligenceBackpropagationBayesian Machine LearningBias-Variance TradeoffBinary Classification AIChatbotsClustering in Machine LearningComposite AIConfirmation Bias in Machine LearningConversational AIConvolutional Neural NetworksCounterfactual Explanations in AICurse of DimensionalityData LabelingDeep LearningDeep Reinforcement LearningDifferential PrivacyDimensionality ReductionEmbedding LayerEmergent BehaviorEntropy in Machine LearningEthical AIExplainable AIF1 Score in Machine LearningF2 ScoreFeedforward Neural NetworkFine Tuning in Deep LearningGated Recurrent UnitGenerative AIGraph Neural NetworksGround Truth in Machine LearningHidden LayerHuman Augmentation with AIHyperparameter TuningIntelligent Document ProcessingLarge Language Model (LLM)Loss FunctionMachine LearningMachine Learning in Algorithmic TradingModel DriftMultimodal LearningNatural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Neural Text-to-Speech (NTTS)NeuroevolutionObjective FunctionPrecision and RecallPretrainingRecurrent Neural NetworksTransformersUnsupervised LearningVoice CloningZero-shot Classification ModelsMachine Learning NeuronReproducibility in Machine LearningSemi-Supervised LearningSupervised LearningUncertainty in Machine Learning
Models
Packages
Techniques
Acoustic ModelsActivation FunctionsAdaGradAI AlignmentAI Emotion RecognitionAI GuardrailsAI Speech EnhancementArticulatory SynthesisAssociation Rule LearningAttention MechanismsAugmented IntelligenceAuto ClassificationAutoencoderAutoregressive ModelBatch Gradient DescentBeam Search AlgorithmBenchmarkingBoosting in Machine LearningCandidate SamplingCapsule Neural NetworkCausal InferenceClassificationClustering AlgorithmsCognitive ComputingCognitive MapCollaborative FilteringComputational CreativityComputational LinguisticsComputational PhenotypingComputational SemanticsConditional Variational AutoencodersConcatenative SynthesisConfidence Intervals in Machine LearningContext-Aware ComputingContrastive LearningCross Validation in Machine LearningCURE AlgorithmData AugmentationData DriftDecision IntelligenceDecision TreeDeepfake DetectionDiffusionDomain AdaptationDouble DescentEnd-to-end LearningEnsemble LearningEpoch in Machine LearningEvolutionary AlgorithmsExpectation MaximizationFeature LearningFeature SelectionFeature Store for Machine LearningFederated LearningFew Shot LearningFlajolet-Martin AlgorithmForward PropagationGaussian ProcessesGenerative Adversarial Networks (GANs)Genetic Algorithms in AIGradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGrapheme-to-Phoneme Conversion (G2P)GroundingHuman-in-the-Loop AIHyperparametersHomograph DisambiguationHooke-Jeeves AlgorithmHybrid AIImage RecognitionIncremental LearningInductive BiasInformation RetrievalInstruction TuningKeyphrase ExtractionKnowledge DistillationKnowledge Representation and Reasoningk-ShinglesLatent Dirichlet Allocation (LDA)Learning To RankLearning RateLogitsMachine Learning Life Cycle ManagementMachine Learning PreprocessingMachine TranslationMarkov Decision ProcessMetaheuristic AlgorithmsMixture of ExpertsModel InterpretabilityMonte Carlo LearningMultimodal AIMulti-task LearningMultitask Prompt TuningNaive Bayes ClassifierNamed Entity RecognitionNeural Radiance FieldsNeural Style TransferNeural Text-to-Speech (NTTS)One-Shot LearningOnline Gradient DescentOut-of-Distribution DetectionOverfitting and UnderfittingParametric Neural Networks Part-of-Speech TaggingPooling (Machine Learning)Principal Component AnalysisPrompt ChainingPrompt EngineeringPrompt TuningQuantum Machine Learning AlgorithmsRandom ForestRectified Linear Unit (ReLU)RegularizationRepresentation LearningRestricted Boltzmann MachinesRetrieval-Augmented Generation (RAG)RLHFSemantic Search AlgorithmsSemi-structured dataSentiment AnalysisSequence ModelingSemantic KernelSemantic NetworksSpike Neural NetworksStatistical Relational LearningSymbolic AITopic ModelingTokenizationTransfer LearningVanishing and Exploding GradientsVoice CloningWinnow AlgorithmWord Embeddings
Last updated on June 24, 20241 min read

LLM Collection

The table below includes some of the most influential models, and the order is sorted by their release dates.

Model Release Date Developer License Description
GPT-4 2023 OpenAI Custom Successor to GPT-3, built on a similar architecture but with improvements.
GPT-3 June 2020 OpenAI Custom 175 billion parameters, known for its versatility and capability.
Turing-NLG February 2020 Microsoft Custom 17 billion parameters, aimed at natural language understanding and generation.
GPT-2 February 2019 OpenAI Modified MIT Initially withheld from public release due to concerns over potential misuse.
BERT October 2018 Google Apache 2.0 Designed to understand the context of words in search queries.
Transformer XL January 2019 Google/CMU Apache 2.0 Extended Transformer model to handle longer sequences of text.
GPT June 2018 OpenAI Modified MIT First Generative Pre-trained Transformer with 117M parameters.
ELMo March 2018 Allen Institute Apache 2.0 Deep contextualized word representations, allowing for rich word meanings.