A mechanism that lets a model weigh how much each part of the input should influence each output, learned rather than fixed.
Read definitionA mechanism that lets a model weigh how much each part of the input should influence each output, learned rather than fixed.
Read definitionThe algorithm that works out how much each weight in a network contributed to the error, by applying the chain rule backwards through the layers.
Read definitionThe optimisation method that trains most models: repeatedly step every parameter a little way downhill on the error surface.
Read definitionA network that processes sequences one element at a time, carrying a hidden state forward — the standard approach before transformers.
Read definitionThe neural network architecture behind almost all modern language models, built around attention instead of recurrence.
Read definition8 subject areas across 32 definitions — start wherever you already are.
The core ideas behind systems that learn patterns from data.
7 termsNeural networks with many layers, and the machinery that trains them.
5 termsHow machines read, generate and reason about human language.
7 termsModels that produce new text, images, audio or code.
4 termsMaking sense of images and video.
1 termDatasets, labelling, evaluation and the training loop itself.
1 termAlignment, bias, interpretability and the limits of these systems.
3 termsServing, scaling and running models in production.
4 terms