The three learned projections in attention mechanisms used to compute attention weights and outputs.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
A model built from layers of simple units, each taking a weighted sum of its inputs and applying a nonlinearity; training adjusts the weights so the whole stack maps inputs to useful outputs.
A collection of data examples used for training, validating, or testing machine learning models.
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
Letting a model learn its own internal description of the data, such as embeddings or layered visual features, instead of relying on features designed by hand.
A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.
A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.
The three learned projections in attention mechanisms used to compute attention weights and outputs.
This concept is essential for understanding large language models and forms a key part of modern AI systems.
Before this
Attention Mechanism