AI glossary
Plain-English definitions for AI and machine learning terms.
Activation function
The nonlinearity applied after each layer's affine map; without it, a network collapses to a single linear function.
Attention
A weighted average of value vectors, with weights computed from query–key similarity, letting each position look at every other.
Backpropagation
The chain rule applied in reverse through a network to compute every parameter's gradient in one pass.
Checkpoint
A saved snapshot of model (and optimizer) state so training can resume or be evaluated later.
Context window
The maximum number of tokens a model can attend over in one forward pass.
Embedding
A learned dense vector standing in for a discrete token, item, or entity.
Fine-tuning
Continuing training of a pretrained model on a narrower dataset to specialize it.
Gradient
The vector of partial derivatives of the loss with respect to the parameters; the direction of steepest increase.
Inference
Using a trained model to produce outputs — as opposed to training it.
KV cache
The stored key/value tensors from earlier tokens so decoding does not recompute them.
LoRA
Low-rank adaptation: fine-tuning by learning small rank-decomposed updates instead of full weight matrices.
Loss
The scalar a training run minimizes, measuring disagreement between predictions and targets.
Overfitting
When a model memorizes training detail that does not generalize to unseen data.
Pretraining
The large, generic first training phase before any task-specific tuning.
RLHF
Reinforcement learning from human feedback: tuning a model against a learned preference model.
Softmax
Turning a vector of scores into a probability distribution over classes.
Temperature
A scaling knob on logits before softmax; higher makes outputs flatter, lower sharper.
Token
The atomic unit of model input — a word piece, character run, or byte span depending on the tokenizer.