Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Machine Learning
Active Learning
A machine learning paradigm where the model can query a user to obtain labels for new data points actively.
Security
adversarial-example
An input designed to fool a machine learning model into making incorrect predictions.
Machine Learning
Bagging
An ensemble method that improves the stability and accuracy of machine learning algorithms.
Machine Learning
contextual-bandits
A form of machine learning that balances exploration and exploitation in dynamic environments.
Data Processing
Data Annotation
The process of labeling data for training machine learning models.
Data Preparation
Data Normalization
The process of scaling individual data points to a common scale, often to improve the performance of machine learning models.
Data Management
Dataset
A structured collection of data used for analysis and training machine learning models.
Machine Learning
Deep Learning
A subfield of machine learning that uses neural networks with many layers.
Machine Learning
distributed-learning
A machine learning paradigm where the training data is distributed across multiple devices or nodes.
Machine Learning
domain-adaptation
A technique in machine learning that aims to improve model performance on a target domain by leveraging labeled data from a related source domain.
Machine Learning
Feature Vector
A numerical representation of an object's characteristics used in machine learning.
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
Gradient Boosting
A machine learning technique that builds models in a sequential manner.
Optimization
Gradient Descent
An optimization algorithm used to minimize the loss function in machine learning.
Machine learning
Graph Machine Learning
Graph machine learning applies statistical and neural methods to data represented as nodes, edges, and attributes so models can learn from relationships as well as individual records.
Machine Learning
incremental-learning
A machine learning approach that updates models continuously with new data without retraining from scratch.
Machine Learning
Machine Learning
A subset of AI that enables systems to learn from data and improve over time.
Machine Learning
modalities
Different forms or types of data used in machine learning, such as text, images, or audio.
Machine Learning Operations
Model Deployment
The process of making a trained machine learning model available for use in a production environment.
Machine Learning
Model Generalization
The ability of a machine learning model to perform well on unseen data.
Machine Learning
Model Training
The process of teaching a machine learning model to make predictions based on data.
Machine Learning
model-complexity
A measure of the capacity of a machine learning model to fit a wide variety of functions.
optimization
model-compression
Techniques for reducing the size and complexity of machine learning models while maintaining performance.