Tag: Machine Learning
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GIGO – Garbage In, Garbage Out
The principle “Garbage In, Garbage Out” (GIGO) asserts the essential link between input data quality and output reliability, emphasizing the need for careful data validation. Rooted in computing history, its relevance spans across fields, advocating for meticulous data handling to ensure accurate outcomes.
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Chomsky Hierarchy
Introduced by Noam Chomsky in the 1950s, the Chomsky Hierarchy categorizes language grammars, providing a framework for analyzing language structures. This concept has profoundly impacted fields like linguistics, computer science, and artificial intelligence, aiding in the comprehension of language and computation.
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LLM – Large Language Model
Large Language Models (LLMs) are sophisticated AI systems designed for language processing. They learn from vast text datasets, excelling in tasks like translation and content creation, and continuously evolve based on new data and user interactions.
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Causal Inference
Causal inference provides a framework for deducing the relationship between cause and effect using empirical data. It employs a variety of rigorous methods to ensure the validity of its findings, making it indispensable in fields such as policy evaluation, economics, and healthcare.
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Decision Tree
A Decision Tree is a graphical tool used to map complex decision-making processes, showcasing different paths and their outcomes. It’s useful for handling uncertainty, risk analysis, and sequential decisions, but can be complicated or misleading if not used properly.