Random Forest
Random Forest is an ensemble of many decision trees whose combined vote produces more robust predictions.
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Gradient Boosting
Gradient Boosting is an ensemble technique that builds models sequentially, each correcting the errors of the…
Ensemble Learning
Ensemble Learning is combining multiple models to produce better predictions than any single model alone.
Overparameterization
Overparameterization is using a model with far more parameters than strictly needed, common in modern deep…
Quantization
Quantization is reducing the numerical precision of a model to make it smaller and faster with…
Distillation
Distillation is training a smaller model to mimic a larger one so it runs faster while…
Explainability
Explainability is the degree to which a human can understand why an AI model made a…
Guardrails
Guardrails is rules and filters that constrain what an AI system is allowed to say or…
Synthetic Data
Synthetic Data is artificially generated data used to train or test models when real data is…
Data Labelling
Data Labelling is the process of annotating data with the correct answers so it can be…
Data Augmentation
Data Augmentation is expanding a dataset by creating modified copies of existing examples to improve model…
Foundation Model
Foundation Model is a large model trained on broad data that can be adapted to many…
Agent
Agent is an AI system that can take actions, use tools, and pursue goals across multiple…
AGI
AGI is artificial general intelligence, a hypothetical AI able to perform any intellectual task a human…
Turing Test
Turing Test is a test of whether a machine's conversation is indistinguishable from a human's.
Knowledge Graph
Knowledge Graph is a structured network of entities and the relationships between them, used to represent…
Semantic Search
Semantic Search is searching by meaning rather than exact keywords, powered by embeddings.
F1 Score
F1 Score is a metric that balances precision and recall to evaluate classification performance.
Precision
Precision is of the items a model flagged as positive, the fraction that were actually correct.