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What Is Few-Shot Learning? | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra 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 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 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 LLM training Overview Loss function Training data Model parameters 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 machine learning AI model artificial intelligence zero-shot learning supervised learning pre-trained models generative AI classification IBM Privacy Statement semi-supervised learning convolutional neural networks (CNNs)computer vision overfitting self-supervised pretext tasks foundation models neural network “catastrophic forgetting”variational autoencoders (VAEs)contrastive self-supervised learning meta learning hyperparameters learning rate model parameters K-nearest neighbors cosine distance Euclidian distance label propagation Watch all episodes of Mixture of Experts backpropagation and gradient descent AI model architecture long-short term memory (LSTM) networks variational autoencoders (VAEs)computer vision semantic segmentation natural language processing large language models Ebook Data science and MLOps for data leaders Join forces with other leaders to drive the three essential pillars of MLOps and trustworthy AI: trust in data, trust in models and trust in processes. Read the ebook Training Level up your ML expertise Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Explore ML courses Ebook Unlock the power of generative AI + ML Learn how to confidently incorporate generative AI and machine learning into your business. Read the ebook Techsplainers Podcast Machine learning explained Techsplainers by IBM breaks down the essentials of machine learning, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen now 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 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 AI models Explore IBM Granite IBM® Granite® is 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. Meet Granite 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 development tools Explore AI services Explore watsonx Orchestrate Explore watsonx.ai "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms""A survey on semi-supervised learning"Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data "Semantic Segmentation with Active Semi-Supervised Learning""Semi-supervised learning by Entropy Minimization""Density-based semi-supervised clustering""Semi-Supervised Learning with Ladder Networks""Learning with Pseudo-Ensembles""Temporal Ensembling for Semi-Supervised Learning""Improved Techniques for Training GANs"

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