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What are Diffusion Models? | 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 Dave Bergmann Cole Stryker neural networks deep learning generative AI machine learning variational autoencoders (VAEs),artificial intelligence model IBM Privacy Statement Langevin dynamics energy-based model gradient Gaussian the U-Net image segmentation evidence lower bound (ELBO)Go to episode denoising autoencoder Gaussian Markov chain his blog post about score-based generative models identity matrix variational autoencoders (VAEs).Kullback-Leibler divergence loss function mean-squared error (MSE)loss function backpropagation gradient descent vector embeddings CLIP zero-shot latent space 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 IBV 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. Read the report Training Take your gen AI skills to the next level Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Learn generative AI Guide 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. Read the guide Report 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. Read the report Techsplainers Podcast 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. Listen now Guide 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. Read the guide Training watsonx® Developer Hub Explore essential tools and resources to accelerate your next project. Get started and discover the full range of supported models available from IBM. Get started Report 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. Read the report AI models 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. Meet Granite Ebook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebook Guide How to thrive in this new era of AI with trust and confidence Dive into the 3 critical elements of a strong AI strategy: creating a competitive edge, scaling AI across the business and advancing trustworthy AI. Read the guide Explore watsonx Orchestrate Explore AI solutions Explore AI services Explore watsonx Orchestrate Explore watsonx.ai “Improved Denoising Diffusion Probabilistic Models,”“Variational Diffusion Models,”

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