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Data Compression and Entropy 2: Entropy as 'Question Game'
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Information Theory: Review and Mutual Information
Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.
Data Compression: Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.
Data Compression and Entropy Definition
Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.
Data Compression and Shannon's Theorem: Shannon-Fano Coding
Explores Shannon-Fano coding for efficient data compression and its comparison to Huffman coding.
Data Compression and Entropy: Illustrating Entropy Properties
Explores entropy as a measure of disorder and how it can be increased.
Lossless Compression: Shannon-Fano and Huffman
Explores lossless compression using Shannon-Fano and Huffman algorithms, showcasing Huffman's superior efficiency and speed over Shannon-Fano.
Shannon's Theorem
Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.
Information Theory: Basics and Applications
Covers the basics of information theory and its applications in various fields.
