Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
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.
Regularization
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly deactivating a fraction of neurons during training.
Ethics
Explainability
The ability to understand and interpret how AI models make decisions.
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 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.
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.
Artificial Intelligence
Generative AI
AI systems that can create new content, such as text, images, or music.
Machine Learning
Generative Modeling
A type of modeling that generates new data instances that resemble the training data.
Deep Learning
generative-adversarial-networks
A class of machine learning frameworks that generate new data samples via adversarial training.
machine-learning
generative-models
Models that can generate new data instances similar to the training data.
Machine Learning
Hyperparameter Tuning
The process of optimizing the parameters that govern the training process of a machine learning model.
Machine Learning
Hyperparameters
Settings or configurations that govern the training process of a machine learning model.
Machine Learning
incremental-learning
A machine learning approach that updates models continuously with new data without retraining from scratch.
Models
Indic LLM
An Indic LLM is a language model optimized for Indian languages, scripts, romanized text, code-mixing, and India-specific cultural or domain context.
Agent security
Indirect prompt injection
Indirect prompt injection happens when untrusted external content, such as a webpage, email, document, or tool result, contains instructions that try to steer an AI system.
Data Management
information-retrieval
The process of obtaining information system resources that are relevant to an information need.
Data Representation
Knowledge Graph
A structured representation of knowledge that connects entities and their relationships, typically used in AI for information retrieval.
Models
Large language model
A large language model, or LLM, is a neural text model trained on large corpora to predict, generate, transform, and reason over language and code.