Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
Training
leaderboard
leaderboard is a core generative-AI concept used across modeling, product, and governance discussions.
Model Training
Learning Rate
The learning rate is a hyperparameter that controls how much to change the model weights in response to the estimated error each time the model weights are updated.
Inference
logit
A logit is an unnormalized score for a vocabulary item before softmax turns it into a probability.
Training
long context
long context is a core generative-AI concept used across modeling, product, and governance discussions.
Training
LoRA
LoRA is a core generative-AI concept used across modeling, product, and governance discussions.
Safety
LoRA for diffusion
LoRA for diffusion is a core generative-AI concept used across modeling, product, and governance discussions.
Evaluation Metrics
Loss Function
A method of evaluating how well a specific algorithm models the given data.
Machine Learning
Machine Learning
A subset of AI that enables systems to learn from data and improve over time.
Safety
memorization
memorization is a core generative-AI concept used across modeling, product, and governance discussions.
Optimization
meta-heuristics
A class of optimization algorithms that use iterative processes to find solutions.
Machine Learning
Meta-Learning
Learning to learn, where models improve their learning strategies over time.
Inference
mixture of experts
mixture of experts is a core generative-AI concept used across modeling, product, and governance discussions.
Training
MMLU
MMLU is a core generative-AI concept used across modeling, product, and governance discussions.
Machine Learning
modalities
Different forms or types of data used in machine learning, such as text, images, or audio.
Inference
model card
model card is a core generative-AI concept used across modeling, product, and governance discussions.
Machine Learning Operations
Model Deployment
The process of making a trained machine learning model available for use in a production environment.
Machine Learning
Model Ensemble
A technique that combines multiple models to improve overall performance.
Model Assessment
Model Evaluation
The process of assessing a trained model's performance using various metrics.
Machine Learning
Model Generalization
The ability of a machine learning model to perform well on unseen data.
Ethics
Model Interpretability
The degree to which a human can understand the cause of a decision made by a model.
Machine Learning
Model Regularization
A technique used to prevent overfitting by adding a penalty for larger coefficients in a model.
Machine Learning
Model Training
The process of teaching a machine learning model to make predictions based on data.
Model Evaluation
Model Validation
The process of evaluating the performance of a model using unseen data.
Machine Learning
model-complexity
A measure of the capacity of a machine learning model to fit a wide variety of functions.