Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Regularization
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly deactivating a fraction of neurons during training.
algorithm-design
dynamic-programming
A method for solving complex problems by breaking them down into simpler subproblems.
Computing
edge computing
Edge computing is a computing paradigm that brings computation and data storage closer to the location of the data source.
Product
embedding
An embedding maps text or media into a dense vector so similarity and retrieval can be computed geometrically.
Probabilistic Models
energy-based-model
A probabilistic model that associates a scalar energy value with each configuration of variables to model distributions.
Learning Techniques
Ensemble Learning
A technique that combines multiple models to improve overall performance.
Machine Learning
Ensemble Method
A technique that combines multiple models to improve performance.
Modeling Techniques
ensemble-methods
Techniques that combine multiple models to improve overall performance.
AI Safety and Governance
Explainability
Explainability describes how clearly people can understand why an AI system produced a particular output or decision.
AI Ethics
Explainable AI
A branch of artificial intelligence focused on making the decision-making processes of models understandable to humans.
robotics
explainable-robotics
A field of study focused on making robotic systems understandable and transparent to users.
Data Analysis
exploratory-data-analysis
An approach to analyzing data sets to summarize their main characteristics.
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.
Deep Learning
Generative Adversarial Network
A class of machine learning frameworks where two neural networks contest with each other to create new data instances.
Neural Networks
Generative Adversarial Network (GAN)
A generative adversarial network (GAN) trains a generator and a discriminator in opposition so the generator learns to produce samples that resemble a training distribution.