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What Are State Space Models? | 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 inspectionmachine learningMambaneural networktransformerstime seriesdeep learningparameterslarge language models (LLMs)vectorIBM Privacy Statementlatentfirst-order differential equationsfirst order derivativecontrol theorytraining dataseteigenvalues and eigenvectorsKalman filterGo to episodeexpectation maximization algorithmsN4SIDloss functionBackpropagationGradient descentsupervised learningself-supervised learningzero order hold (ZOH)recurrent neural network (RNN)model trainingconvolutional neural networks (CNNs)HiPPOsequential MNIST benchmarkautoregressivethe S4 paper demonstratesconvolutionfast Fourier transformMambaGuide Start realizing ROI: A practical guide to agentic AI Learn how to scale agentic AI for measurable ROI across your enterprise. This playbook outlines the top barriers that limit impact, how to effectively measure ROI and a practical framework to drive successful, enterprise-wide adoption. Get the guideGuide The CEO's guide to model optimization Learn how to continually push teams to improve model performance and outpace the competition by using the latest AI techniques and infrastructure. Read the guideTraining watsonx® Developer Hub Support your next project with some of our most commonly used capabilities. Get started and learn more about the supported models that IBM provides. Get startedReport A differentiated approach to AI foundation models Explore the value of enterprise-grade foundation models that provide trust, performance and cost-effective benefits to all industries. Read the reportEbook Unlock the power of generative AI and ML Learn how to incorporate generative AI, machine learning and foundation models into your business operations for improved performance. Read the ebookInsight How IBM is tailoring generative AI for enterprises Learn how IBM is developing generative foundation models that are trustworthy, energy efficient and portable. Read the insightExplore GraniteExplore AI solutionsExplore AI servicesDiscover watsonx.aiExplore IBM Granite AI models"The Apollo 11 Moon Landing: Spacecraft Design Then and Now,""A guide to state–space modeling of ecological time series,"

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