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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 inspectionretrieval-augmented generation (RAG)1workflowsIBM Privacy Statementvector databasesgenerative AIknowledge graphmachine learningembeddingsGenerative AIvector searchtext summarizationchatbotsGo to episodeunstructured dataLangChainLlamaIndextutorialsmetadataGPTAPIEbook Unlock the power of generative AI and ML Learn how to confidently incorporate generative AI and machine learning into your business. Read the ebookGo to episodeTutorial IBM Developer: RAG tutorials Explore all IBM Developer retrieval augmented generation (RAG) tutorials. Start learningTechsplainers Podcast Retrieval augmented generation (RAG) explained Techsplainers by IBM breaks down the essentials of RAG, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen nowBlog IBM RAG Cookbook Explore a comprehensive collection of best practices, considerations and tips for building RAG solutions tailored to business applications. Read the blogArticle IBM Developer: RAG articles Explore all IBM Developer retrieval augmented generation (RAG) articles. Get startedArchitecture Retrieval augmented generation (RAG) architecture Discover proven architecture patterns that accelerate the creation of technology solutions to meet your business challenges. Explore architectureTutorial Build a RAG agent to answer complex questions Use Python, LangGraph, watsonx.ai®, Elasticsearch and Tavily to build a customized, modular agentic AI system. Start learningTutorial Quick start: Prompt a foundation model with the retrieval-augmented generation pattern Learn how to use foundation models in IBM watsonx.ai to generate factually accurate output grounded in information in a knowledge base by applying the retrieval augmented generation pattern. Start learningExplore watsonx OrchestrateExplore AI development toolsExplore AI servicesExplore watsonx OrchestrateExplore AI development tools

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