705,632 AI resources indexedWorking toward the world’s largest AI resource index.

DaveAI
Saved 0Contribute
Using Dave AI

AI glossary

Plain-language explanations of AI terms and acronyms.

200 terms & acronyms

Terms & definitions

45 of 200 terms

Clear filters

B

Backpropagation
A training method that works backward through a neural network to calculate how its adjustable values contributed to an error.
Batch
A group of examples processed together during training, or a group of requests processed together during inference. Batching can improve hardware efficiency.
Batch size
The number of examples or requests processed together. A larger batch often needs more memory and can change training behavior or response timing.

C

Checkpoint
A saved state of a model during or after training. It allows training to resume or a particular model version to be used later.
Clustering
Grouping similar examples without requiring a category label for every example. It can help reveal patterns in a dataset.
Contrastive learning
Training a model to bring related examples closer together in its representation and push unrelated examples farther apart.

D

Data augmentation
Creating additional training examples by changing existing ones, such as cropping an image. The changes should preserve the information relevant to the task.
Data labeling
Adding the answers or categories a model is meant to learn from. Labels might identify an object in an image or mark whether a response is helpful.
Dataset
A collection of examples used for training, evaluation, or analysis. A dataset can contain text, images, audio, structured records, or a mixture of formats.
Distillation
Training one model to learn from the outputs or behavior of another model. It is often used to make a smaller or faster model approximate a larger one.
DPODirect preference optimization
Adapting a model using examples of preferred and less-preferred responses. It learns from those comparisons without a separate reinforcement-learning loop of the usual RLHF kind.
Dropout
A training technique that temporarily disables selected parts of a neural network to reduce overreliance on particular connections.

E

Epoch
One complete pass through a training dataset. Training may involve several epochs, depending on the data and method.

F

Feature
An input property used by a model, such as a measurement, word pattern, or representation learned from data.
Fine-tuning
Further training an existing model on additional examples to adapt its behavior or task performance. This changes model parameters, unlike simply supplying a longer prompt.

G

Gradient
A calculation describing how a change in a model's adjustable values would affect its training error. Training uses gradients to choose updates.
Gradient descent
An optimization method that repeatedly adjusts model parameters in a direction intended to reduce training error.

H

Hyperparameter
A setting chosen to control a training or model process, rather than a value learned directly from examples. Learning rate and batch size are common training hyperparameters.

I

Instruction tuning
Training a model on examples of instructions and desired responses so it becomes better at following requests.

L

Label
An expected answer or category attached to a training example. An image might be labeled as containing a bicycle.
Learning rate
A setting that controls the size of parameter updates during training. Updates that are too large or too small can make training ineffective.
LoRALow-rank adaptation
A fine-tuning technique that trains small added parameter sets while keeping most of the original model fixed. It can make adaptation less expensive than updating every parameter.
Loss
A numerical measure of how far a training result is from the desired result. Training aims to improve this measure, but a lower loss does not automatically mean better real-world behavior.

M

Model merging
Combining parameters or adaptations from multiple models to create another model. Success depends on the models and method; it does not simply combine all their abilities.

O

Optimizer
An algorithm that decides how to update model parameters during training using information such as gradients. Adam and stochastic gradient descent are common examples.
Overfitting
When a model learns training examples so closely that it performs poorly on new examples. Strong training results alone do not prove that a model generalizes well.

P

PEFTParameter-efficient fine-tuning
Methods that adapt a model by training a relatively small set of parameters. LoRA is one example.
Pretraining
The initial broad training used to build a model before more specific adaptation. For many language models, it involves learning to predict text from large collections of examples.
Pruning
Removing or disabling selected model parameters or structures to reduce size or computation. Quality depends on what is removed and how the model is adapted afterward.

Q

QLoRAQuantized low-rank adaptation
A fine-tuning approach that combines a quantized base model with trainable LoRA adapters to reduce the memory needed for adaptation.

R

Regression
Predicting a numerical value, such as a price or temperature, rather than assigning only a category. In software testing, regression instead means a previously working behavior breaks.
Reinforcement learning
Training an agent or model using feedback about the outcomes of its actions. The goal is to learn behavior that achieves more reward over time.
RLHFReinforcement learning from human feedback
A family of methods using human preference information to guide behavior through reinforcement learning. Human feedback is often used to train a reward model first.

S

Self-supervised learning
Learning from training targets derived from the data itself, rather than a separate human label for every example. Predicting the next token in text is one example.
SFTSupervised fine-tuning
Adapting a model with examples of inputs and desired outputs. It is commonly used to teach response styles, formats, or instruction-following behavior.
Supervised learning
Training with examples that include expected answers or labels. The model learns to map inputs to those supplied targets.
Synthetic data
Data produced artificially rather than collected directly from the original real-world process. It can help with training or testing, but may introduce its own errors or biases.

T

Test set
Examples held back to evaluate a model after training and model selection. Repeatedly tuning to a test set weakens its value as an independent check.
Training
Adjusting a model using data and a learning objective. Training changes the model, while ordinary inference applies an existing model to an input.
Training data
The examples used to adjust a model during training. A model can learn useful patterns and also inherit gaps, errors, or biases from those examples.
Training split
The portion of a dataset used to fit a model. Separate validation and test splits help assess decisions and performance on examples not used for that fitting.
Transfer learning
Using a model or representation learned for one setting as a starting point for another task or dataset.

U

Underfitting
When a model has not learned enough of the relevant patterns to perform well, including on its training examples.
Unsupervised learning
Learning patterns from data without an expected answer label for every example. Clustering is one common example.

V

Validation set
Examples used to guide training choices or model selection without directly fitting the model to those examples in the usual training loop. A separate test set supports final evaluation.