attention mechanism
An attention mechanism is a neural network operation that computes a weighted sum of value vectors based on the similarity between a query and a set of keys. This mechanism allows models to focus on the most relevant information in or across sequences.
Variants include self-attention for intra-sequence dependencies and cross-attention to condition a decoder on encoder outputs. Earlier sequence-to-sequence models introduced additive and global or local attention to address fixed-length bottlenecks, while transformers popularized multi-head self-attention, masking for causal generation, and encoder–decoder cross-attention.
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Embeddings and Vector Databases With ChromaDB
Vector databases are a crucial component of many NLP applications. This tutorial will give you hands-on experience with ChromaDB, an open-source vector database that's quickly gaining traction. Along the way, you'll learn what's needed to understand vector databases with practical examples.
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By Leodanis Pozo Ramos • Updated Sept. 14, 2026
