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Neural Networks: Tricks of the Trade (Record no. 14541)

000 -LEADER
fixed length control field 04869nam a22005415i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310144113.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121116s2012 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642352898
978-3-642-35289-8
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA75.5-76.95
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 004.0151
Edition number 23
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2012.
912 ## -
-- ZDB-2-SCS
-- ZDB-2-LNC
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Montavon, Grégoire.
Relator term editor.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Neural Networks: Tricks of the Trade
Medium [electronic resource] :
Remainder of title Second Edition /
Statement of responsibility, etc edited by Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller.
300 ## - PHYSICAL DESCRIPTION
Extent XII, 769 p. 223 illus.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Lecture Notes in Computer Science,
International Standard Serial Number 0302-9743 ;
Volume number/sequential designation 7700
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction -- Preface on Speeding Learning -- 1. Efficient BackProp -- Preface on Regularization Techniques to Improve Generalization -- 2. Early Stopping — But When? -- 3. A Simple Trick for Estimating the Weight Decay Parameter -- 4. Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework.- 5. Adaptive Regularization in Neural Network Modeling -- 6. Large Ensemble Averaging -- Preface on Improving Network Models and Algorithmic Tricks -- 7. Square Unit Augmented, Radially Extended, Multilayer Perceptrons -- 8. A Dozen Tricks with Multitask Learning -- 9. Solving the Ill-Conditioning in Neural Network Learning -- 10. Centering Neural Network Gradient Factors -- 11. Avoiding Roundoff Error in Backpropagating Derivatives.- 12. Transformation Invariance in Pattern Recognition –Tangent Distance and Tangent Propagation -- 13. Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newtons -- 14. Neural Network Classification and Prior Class Probabilities -- 15. Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Preface on Tricks for Time Series -- 16. Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- 17. How to Train Neural Networks -- Preface on Big Learning in Deep Neural Networks -- 18. Stochastic Gradient Descent Tricks.- 19. Practical Recommendations for Gradient-Based Training of Deep Architectures -- 20. Training Deep and Recurrent Networks with Hessian-Free Optimization -- 21. Implementing Neural Networks Efficiently -- Preface on Better Representations: Invariant, Disentangled and Reusable -- 22. Learning Feature Representations with K-Means -- 23. Deep Big Multilayer Perceptrons for Digit Recognition -- 24. A Practical Guide to Training Restricted Boltzmann Machines -- 25. Deep Boltzmann Machines and the Centering Trick -- 26. Deep Learning via Semi-supervised Embedding -- Preface on Identifying Dynamical Systems for Forecasting and Control -- 27. A Practical Guide to Applying Echo State Networks -- 28. Forecasting with Recurrent Neural Networks: 12 Tricks -- 29. Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks -- 30. 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers.
520 ## - SUMMARY, ETC.
Summary, etc The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
Topical term or geographic name as entry element Computer software.
Topical term or geographic name as entry element Artificial intelligence.
Topical term or geographic name as entry element Optical pattern recognition.
Topical term or geographic name as entry element Physics.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Computer Science.
Topical term or geographic name as entry element Computation by Abstract Devices.
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
Topical term or geographic name as entry element Algorithm Analysis and Problem Complexity.
Topical term or geographic name as entry element Pattern Recognition.
Topical term or geographic name as entry element Complexity.
Topical term or geographic name as entry element Information Systems Applications (incl. Internet).
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Orr, Geneviève B.
Relator term editor.
Personal name Müller, Klaus-Robert.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642352881
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-35289-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-03-31AUM Main Library2014-03-31 2014-03-31 E-Book   AUM Main Library004.0151
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العربية