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Related lectures (29)

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Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Explores the role of Computing in society and the basics of computing, algorithms, communication systems, and computer security.

Covers source coding theorems, entropy, and various source models in information theory.

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Discusses conditional entropy and its role in data compression techniques.

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Explores entropy as a 'question game' to guess letters efficiently and its relation to data compression.

Discusses conditional entropy and its role in information theory and data compression.

Explores data compression, including lossless methods and the necessity of lossy compression for real numbers and signals.

Covers the definition of entropy, Shannon–Fano algorithm, and upcoming topics.