AI Glossary: Letter "V"

Validation Data is a subset of the dataset held back during machine learning training, used to evaluate model progress, adjust hyperparameters, and prevent overfitting.

The Vanishing Gradient Problem is a training difficulty in deep neural networks where the gradients of the loss function shrink exponentially as they propagate backward to the early layers, preventing the model weights from updating and learning.

A Vector Database is a specialized storage engine designed to store, index, and query high-dimensional vector embeddings efficiently. It enables fast semantic search and similarity matching using algorithms like HNSW or IVF.

A Vision Transformer (ViT) is a neural network architecture that adapts the Transformer attention mechanism for computer vision tasks. By splitting images into grid patches and treating them like tokens in a sentence, ViT learns long-range visual relations.

vLLM is a high-throughput, memory-efficient serving engine for LLMs that utilizes PagedAttention to manage KV cache memory. By dynamically allocating KV cache blocks like virtual memory in operating systems, it eliminates memory fragmentation and increases serving throughput.

A Vision-Language Model (VLM) is a multimodal AI model trained on both images and text, enabling it to answer questions about visual content, describe images, or extract structured data from documents.