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Lecture
Shannon's Theorem
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Related lectures (25)
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Data Compression: Entropy Definition
Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.
Source Coding: Compression
Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.
Data Compression and Entropy 2: Entropy as 'Question Game'
Explores entropy as a 'question game' to guess letters efficiently and its relation to data compression.
Source Coding Theorems: Entropy and Source Models
Covers source coding theorems, entropy, and various source models in information theory.
Stochastic Processes: Sequences and Compression
Explores compression in stochastic processes through injective codes and prefix-free codes.
Entropy and Compression I
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Conditional Entropy: Huffman Coding
Explores conditional entropy and Huffman coding for efficient data compression techniques.
Data Compression and Entropy: Conclusion
Covers the definition of entropy, Shannon–Fano algorithm, and upcoming topics.
Data Compression: Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.
Compression: Kraft Inequality
Explains compression and Kraft inequality in codes and sequences.
