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THEORY OF NEURAL INFORMATION PROCESSING SYSTEMS By A. C. C. Coolen & R. Kuhn
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US $171.95
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“Book is in typical used-Good Condition. Will show signs of wear to cover and/or pages. There may be ”... Read moreabout condition
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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eBay item number:226856645384
Item specifics
- Condition
- Good
- Seller Notes
- ISBN-10
- 0198530242
- Book Title
- Theory of Neural Information Processing Systems
- Genre
- COMPUTERS
- ISBN
- 9780198530244
About this product
Product Identifiers
Publisher
Oxford University Press, Incorporated
ISBN-10
0198530242
ISBN-13
9780198530244
eBay Product ID (ePID)
46742535
Product Key Features
Number of Pages
586 Pages
Language
English
Publication Name
Theory of Neural Information Processing Systems
Subject
Life Sciences / Neuroscience, Neural Networks, System Administration / Storage & Retrieval
Publication Year
2005
Type
Textbook
Subject Area
Computers, Science
Format
Trade Paperback
Dimensions
Item Height
1.3 in
Item Weight
36 Oz
Item Length
9.6 in
Item Width
6.8 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2006-295078
Reviews
The book provides an excellent class-tested material for graduate courses in artificial neural networks. It is completely self-contained and includes also thorough introduction to the discussed discipline-specific areas of mathematics...Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well.
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
006.32
Table Of Content
I Introduction to Neural Networks1. General introduction2. Layered networks3. Recurrent networks with binary neuronsII Advanced Neural Networks4. Competitive unsupervised learning processes5. Bayesian techniques in supervised learning6. Gaussian processes7. Support vector machines for binary classificationIII Information Theory and Neural Networks8. Measuring information9. Identification of entropy as an information measure10. Building blocks of Shannon's information theory11. Information theory and statistical inference12. Applications to neural networksIV Macroscopic Analysis of Dynamics13. Network operation: macroscopic dynamics14. Dynamics of online learning in binary perceptrons15. Dynamics of online gradient descent learningV Equilibrium Statistical Mechanics of Neural Networks16. Basics of equilibrium statistical mechanics17. Network operation: equilibrium analysis18. Gardner theory of task realizabilityAppendicesA. Historical and bibliographical notesB. Probability theory in a nutshellC. Conditions for central limit theorem to applyD. Some simple summation identitiesE. Gaussian integrals and probability distributionsF. Matrix identitiesG. The delta-distributionH. Inequalities based on convexityI. Metrics for parametrized probability distributionsJ. Saddle-point integrationReferences
Synopsis
This interdisciplinary text gives a full, coherent, and up-to-date account of the modern theory of neural information processing systems. Aimed at students with an undergraduate degree in any quantitative discipline it covers all the major theoretical developments from the 1940s to the present day and includes introductions to various mathematical tools as well as multiple exercises on each topic., Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering or biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the student into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience., This interdisciplinary graduate text gives a full, explicit, coherent and up-to-date account of the modern theory of neural information processing systems and is aimed at student with an undergraduate degree in any quantitative discipline (e.g. computer science, physics, engineering, biology, or mathematics). The book covers all the major theoretical developments from the 1940s tot he present day, using a uniform and rigorous style of presentation and of mathematical notation. The text starts with simple model neurons and moves gradually to the latest advances in neural processing. An ideal textbook for postgraduate courses in artificial neural networks, the material has been class-tested. It is fully self contained and includes introductions to the various discipline-specific mathematical tools as well as multiple exercises on each topic.
LC Classification Number
QA76.87
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- w***b (257)- Feedback left by buyer.Past monthVerified purchase👍🏻
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- i***9 (79)- Feedback left by buyer.Past monthVerified purchaseFast shipping, product was as advertised, thanks!