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What Are Large Language Models (LLMs)? | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Data visualization for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Tutorial: Multilingual LLM agent Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Overview Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Cole Stryker deep learning neural network transformer natural language processing artificial intelligence generative AI Claude ChatGPT Llama models Gemini Granite model series watsonx.ai watsonx Assistant watsonx Orchestrate IBM Privacy Statement tokenization self-supervised learning machine learning supervised learning transformer self-attention mechanism AI model datasets embedding positional encodings Self-attention LLM parameter small language models training data loss function backpropagation gradient descent vector embedding chatbot domain-specific customization reinforcement learning from human feedback reinforcement learning reasoning model LLM customization instruction tuning inference prompt engineering LLM temperature context window Retrieval augmented generation pretrained model Go to episode GPUs pretrained models automation Hugging Face agentic system APIs Text summarization AI assistants conversational AI question answering Code generation Sentiment analysis hallucinations biases AI governance LLM benchmarks encoder–decoder Mamba models Diffusion LLMs Spotify Apple Podcasts Find more episodes Cole Stryker Upcoming webinar - May 20, 2026 Upcoming webinar on AI‑native development Engineering teams are strained by legacy tools and tech debt. Discover how an AI‑native SDLC helps teams move faster while reducing risk. Join now See watsonx Orchestrate in action Ready to scale AI the right way?IBM Bob Meet Bob™, your AI development partner An AI powered tool that helps you code, debug and deliver high quality software without breaking your flow. Start building with Bob Guide 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 guide AI models Explore IBM Granite Discover IBM Granite®, 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. Learn more IBM TechXchange IBM AI community Join the community for Al architects and builders to learn, share ideas and connect with others. Find your community Training IBM watsonx® Developer Hub Support your next project with some of our most used capabilities. Get started and learn more about the supported models that IBM provides. Get started Techsplainers podcast Large language models explained Techsplainers by IBM breaks down the essentials of LLMs, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen now Discover watsonx.ai Explore AI development tools Explore AI services Explore watsonx.ai Explore AI development tools Attention is all you need

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