What is entropy in information theory?
An example of an AI chatbot conversation about entropy and its role in information theory.
What is entropy in information theory?
Entropy, in information theory, measures the uncertainty or unpredictability of information content.
How is entropy calculated?
Entropy is calculated using the formula: H(X) = -Σ p(x) log₂ p(x), where p(x) is the probability of each outcome.
Can you give an example of entropy in real life?
Sure! In a fair coin toss, the entropy is high because the outcome is unpredictable. In contrast, a biased coin has lower entropy.
What is the significance of entropy in data compression?
Entropy helps determine the minimum number of bits needed to encode information, guiding efficient data compression techniques.
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