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Shannon's source coding theorem
Applied sciences
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Related lectures (27)
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Information Theory: Source Coding, Cryptography, Channel Coding
Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.
Random Coding: Achievability and Proof Variants
Explores random coding achievability and proof variants in information theory, emphasizing achievable rates and architectural principles.
Information Theory: Source Coding
Covers source coding, typical sequences, stationarity, and efficient encoding in information theory.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Information Theory: Source Coding & Channel Coding
Covers the fundamentals of information theory, focusing on source coding and channel coding.
Source Coding Theorem
Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.
Shannon's Theorem
Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.
Data Compression and Shannon's Theorem: Shannon's Theorem Demonstration
Covers the demonstration of Shannon's theorem, focusing on data compression.
Information Theory and Coding: Source Coding
Covers source coding, encoder design, and error probability analysis in information theory and coding.
Data Compression and Shannon's Theorem Summary
Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.
