Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
Safety
faithfulness
faithfulness is a core generative-AI concept used across modeling, product, and governance discussions.
Data Preparation
Feature Engineering
The process of selecting, modifying, or creating features from raw data.
Model Interpretation
Feature Importance
A measure of how much a feature contributes to the predictive power of a model.
Machine Learning
Feature Mapping
The process of transforming input features into a more suitable format for modeling.
Data Preprocessing
Feature Scaling
The process of standardizing or normalizing features so they contribute equally to the model.
Machine Learning
Feature Vector
A numerical representation of an object's characteristics used in machine learning.
Data Processing
feature-extraction
The process of transforming raw data into a set of usable characteristics for model training.
Data Processing
feature-selection
The process of selecting a subset of relevant features for model training.
Machine Learning
Federated Learning
A machine learning approach that allows models to be trained across decentralized devices or servers holding local data samples.
Machine Learning
few-shot-learning
A machine learning paradigm that trains models with very few labeled examples.
Machine Learning
fine-tuning
The process of adjusting a pre-trained model on a new, often smaller dataset to improve performance on a specific task.
Training
fine-tuning dataset
fine-tuning dataset is a core generative-AI concept used across modeling, product, and governance discussions.
Inference
flash attention
flash attention is a core generative-AI concept used across modeling, product, and governance discussions.
Product
full fine-tune
full fine-tune is a core generative-AI concept used across modeling, product, and governance discussions.
Inference
function calling
function calling is a core generative-AI concept used across modeling, product, and governance discussions.