How learning reshapes the way we represent knowledge and build smarter machines.
This book analyzes learning as a practical, data-driven process. It weaves ideas from information theory with how codes and classifications interact to produce stable, unique representations of text and problems. The goal is to understand how to make learning efficient and computer implementations reliable.
Readers will explore concepts like semantic alphabets, complete schemes, and hierarchical alphabets, and how these ideas support flexible, adaptive computation. The work also touches on neural networks, language processing, and the broader implications for programming languages and computer systems.
Ideal for readers of learning theory, information science, and computational linguistics who want a foundational, theory-driven view of how representations evolve.
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Seller: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LX-9780484499514
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LX-9780484499514
Quantity: 15 available