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 feedbackDeep reinforcement learningOverviewOverviewBackpropagationEncoder-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 customizationLLM alignmentTutorial: Multilingual LLM agentDiffusion 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 AgentsOverviewLLM trainingLoss functionTraining dataModel parametersOverviewGradient 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 inspectionAnna GutowskaErika RussiPixtral 12Bwatsonx.ailarge language model (LLM)Nemo 12B LLMretrieval augmented generationHugging FaceYouTube videowatsonx.aiwatsonx.ai projectJupyter NotebookGitHubvirtual environmentwatsonx.ai Runtimewatsonx.aiMeta’s multimodal model Llama 3.2Anna GutowskaErika RussiIBV Report
The enterprise in 2030: Engineered for perpetual innovation
Discover our five predictions about what will define the most successful enterprises in 2030 and the steps leaders can take to gain an AI-first advantage.
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Put AI to work: Driving ROI with gen AI
Want to get a better return on your AI investments? Learn how scaling gen AI in key areas drives change by helping your best minds build and deliver innovative new solutions.
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From AI projects to profits: How agentic AI can sustain financial returns
Learn how organizations are shifting from launching AI in disparate pilots to applying AI to drive transformation at the core.
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Generative AI explained
Techsplainers by IBM breaks down the essentials of gen AI, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast.
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The CEO's guide to generative AI
Learn how CEOs can balance the value generative AI can create against the investment it demands and the risks it introduces.
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watsonx® Developer Hub
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The truth about successful generative AI
Uncover the benefits of AI platforms that enable foundation model customization through technology, processes and best practices to help you easily operationalize the gen AI lifecycle.
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Explore IBM Granite
IBM Granite® is our family of open, performant, and trusted AI models designed for business and optimized to scale your AI applications. Explore models for language, code, time series and guardrails.
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How to thrive in this new era of AI with trust and confidence
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