Entropy and Algorithms

Related lectures (29)

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Covers the properties of entropy and demonstrates their application in data compression.

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

Introduces data compression, entropy, and the importance of reducing redundancy in data.

Covers the demonstration of Shannon's theorem, focusing on data compression.

Explores Shannon-Fano coding for efficient data compression and its comparison to Huffman coding.

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

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

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

Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.