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Related lectures (29)
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Data Compression: Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.
Introduction to Data Science
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Data Compression and Shannon's Theorem Summary
Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.
Density of States and Bayesian Inference in Computational Mathematics
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Data Compression and Shannon's Theorem: Entropy Calculation Example
Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.
Data Compression and Entropy: Basics and Introduction
Introduces data compression, entropy, and the importance of reducing redundancy in data.
Data Compression and Shannon's Theorem: Performance Analysis
Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.
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.
Data Compression and Entropy Interpretation
Explores the origins and interpretation of entropy, emphasizing its role in measuring disorder and information content in a system.
