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What is topic modeling? | IBM

WelcomeOverviewMachine learning typesMachine learning algorithmsStatistical machine learningLinear algebra for machine learningUncertainty quantificationBias variance tradeoffBayesian StatisticsSingular value decompositionOverviewFeature selectionFeature extractionVector embeddingLatent spacePrincipal component analysisLinear discriminant analysisUpsamplingDownsamplingSynthetic dataData leakageOverviewLinear regressionLasso regressionRidge regressionState space modelTime seriesAutoregressive modelOverviewDecision treesK-nearest neighbors (KNNs)Naive bayesRandom forestSupport vector machineLogistic regressionOverviewBoostingBaggingGradient boostingGradient boosting classifierOverviewTransfer learningOverviewOverviewK means clusteringHierarchical clusteringA priori algorithmGaussian mixture modelAnomaly detectionOverviewCollaborative filteringContent based filteringOverviewReinforcement learning human feedbackOverviewOverviewBackpropagationEncoder-decoder modelRecurrent neural networksLong short-term memory (LSTM)Convolutional neural networksOverviewAttention mechanismGrouped query attentionPositional encodingAutoencoderMamba modelGraph neural networkOverviewGenerative modelGenerative AI vs. predictive AIOverviewReasoning modelsSmall language modelsInstruction tuningLLM parametersLLM temperatureLLM benchmarksLLM customizationDiffusion modelsVariational autoencoder (VAE)Generative adversarial networks (GANs)OverviewVision language modelsTutorial: Build an AI stylistTutorial: Multimodal AI queries using LlamaTutorial: Multimodal AI queries using PixtralTutorial: Automatic podcast transcription with GraniteTutorial: PPT AI image analysis answering systemOverviewGraphRAGTutorial: Build a multimodal RAG system with Docling and GraniteTutorial: Evaluate RAG pipline using RagasTutorial: RAG chunking strategiesTutorial: Graph RAG using knowledge graphsTutorial: Inference scaling to improve multimodal RAGOverviewVibe codingVisit the 2025 Guide to AI AgentsLLM trainingOverviewLoss functionTraining dataModel parametersGradient descentStochastic gradient descentHyperparameter tuningLearning rateOverviewParameter efficient fine tuning (PEFT)LoRATutorial: Fine tuning Granite model with LoRARegularizationFoundation modelsOverfittingUnderfittingFew shot learningZero shot learningKnowledge distillationMeta learningData augmentationCatastrophic forgettingOverviewScikit-learnXGboostPyTorchOverviewAI lifecyleAI inferenceModel deploymentMachine learning pipelineData labelingModel risk managementModel driftAutoMLModel selectionFederated learningDistributed machine learningAI stackOverviewNatural language understandingOverviewSentiment analysisTutorial: Spam text classifier with PyTorchMachine translationOverviewInformation retrievalInformation extractionTopic modelingLatent semantic analysisLatent Dirichlet AllocationNamed entity recognitionWord embeddingsBag of wordsIntelligent searchSpeech recognitionStemming and lemmatizationText summarizationConversational AIConversational analyticsNatural language generationOverviewImage classificationObject detectionInstance segmentationSemantic segmentationOptical character recognitionImage recognitionVisual inspectionEda Kavlakoglunatural language processingtext miningunsupervised learningNatural Language Toolkit (NLTK)IBM Privacy Statementsupervisedbag of wordsWatch all episodes of Mixture of Expertsstemminglemmatizationreducing sparsity and dimensionalitylinear discriminant analysisMonte Carloartificial intelligencelarge language modelsReport Beyond the hype - How AI assistants drive real business value Explore top use cases for leveraging AI assistants, understand the potential impact of Gen AI and automation technology on your business, and learn how to get started. Read the reportAI models Explore IBM Granite® IBM Granite ® is our family of open, performant and trusted AI models, tailored for business and optimized to scale your AI applications. Explore language, code, time series and guardrail options. Meet GraniteExplainer Beginner's guide to NLP Discover how natural language processing (NLP) can help you to converse more naturally with computers. Learn NLPReport IBM is named leader in data science and machine learning Learn why IBM has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for data science and machine learning platforms. Read the reportTraining Hands-on with generative AI Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Learn generative AIExplore watsonx OrchestrateExplore NLP solutionsExplore AI servicesExplore watsonx OrchestrateExplore NLP solutionshttps://web.stanford.edu/~jurafsky/slp3/https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-4571%28199009%2941%3A6%3C391%3A%3AAID-ASI1%3E3.0.CO%3B2-9https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-4571%28199009%2941%3A6%3C391%3A%3AAID-ASI1%3E3.0.CO%3B2-9https://academic.oup.com/dsh/article/39/1/142/7515230?login=falsehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504675/https://link.springer.com/chapter/10.1007/978-3-031-10464-0_38https://www.sciencedirect.com/science/article/pii/S1877050921012199https://www.jmir.org/2020/12/e22609https://academic.oup.com/dsh/article-abstract/38/2/530/6957052https://dl.acm.org/doi/10.1145/3236386.3241340https://aclanthology.org/2021.naacl-main.300.pdfhttps://proceedings.neurips.cc/paper_files/paper/2021/hash/0f83556a305d789b1d71815e8ea4f4b0-Abstract.htmlhttps://aclanthology.org/2023.emnlp-main.581https://www.sciencedirect.com/science/article/pii/S2949719123000419https://www.computer.org/csdl/proceedings-article/bigdata/2023/10386113/1TUOz14EiBy

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