Compression

Related lectures (28)

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Covers the concepts of compression and prediction using prefix-free codes and distributions.

Explores the relationship between code word length and probability distribution, focusing on designing prefix-free codes for efficient compression.

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Explains compression and Kraft inequality in codes and sequences.

Discusses conditional entropy and its role in data compression techniques.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers probability distributions, moments, and continuous random variables.

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Explores continuous random variables and their properties, including support and cumulative distribution functions.