This book offers a comprehensive journey through the fascinating world of information theory, beginning with the fundamental question: what is information? Early chapters introduce key concepts like entropy, binary representation, and data compression, providing a clear and accessible foundation. Readers explore Shannon's source coding theorem and practical tools like Huffman coding to understand how information is quantified and optimized.
Building on these basics, the book delves into advanced topics such as the noisy channel coding theorem, mutual information, and error correction techniques. It examines entropy in continuous systems, channel capacity, and rate-distortion theory, making complex ideas accessible through real-world examples. Connections between information and thermodynamics are also explored, including Maxwell’s Demon, the Landauer Limit, and the second law of thermodynamics.
The final chapters tie information theory to biology and artificial intelligence, investigating its role in evolution, the human genome, and brain computation. With practical examples throughout, this book balances theoretical depth with hands-on learning, making it an essential resource for mastering information theory. A basic mathematical foundation will be beneficial but is not required to engage with the material.

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Spis treści

Information Theory. A Tutorial Introduction to the Principles and Applications of Information Theory eBook -- spis treści

• 1. What Is Information?
• 2. Entropy of Discrete Variables
• 3. The Source Coding Theorem
• 4. The Noisy Channel Coding Theorem
• 5. Entropy of Continuous Variables
• 6. Mutual Information: Continuous
• 7. Channel Capacity: Continuous
• 8. Rate Distortion Theory
• 9. Transfer Entropy
• 10. Thermodynamic Entropy and Information
• 11. Information As Nature's Currency