000			05029nam a22005775i 4500
001			978-3-642-35289-8
003			DE-He213
005			20140220083331.0
007			cr nn 008mamaa
008			121116s2012 gw | s |||| 0|eng d
020			_a9783642352898 _9978-3-642-35289-8
024	7		_a10.1007/978-3-642-35289-8 _2doi
050		4	_aQA75.5-76.95
072		7	_aUYZG _2bicssc
072		7	_aCOM037000 _2bisacsh
082	0	4	_a004.0151 _223
100	1		_aMontavon, Grégoire. _eeditor.
245	1	0	_aNeural Networks: Tricks of the Trade _h[electronic resource] : _bSecond Edition / _cedited by Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2012.
300			_aXII, 769 p. 223 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x0302-9743 ; _v7700
505	0		_aIntroduction -- 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			_aThe 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	_aComputer science.
650		0	_aComputer software.
650		0	_aArtificial intelligence.
650		0	_aOptical pattern recognition.
650		0	_aPhysics.
650		0	_aEngineering.
650	1	4	_aComputer Science.
650	2	4	_aComputation by Abstract Devices.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aPattern Recognition.
650	2	4	_aComplexity.
650	2	4	_aInformation Systems Applications (incl. Internet).
700	1		_aOrr, Geneviève B. _eeditor.
700	1		_aMüller, Klaus-Robert. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642352881
830		0	_aLecture Notes in Computer Science, _x0302-9743 ; _v7700
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-35289-8
912			_aZDB-2-SCS
912			_aZDB-2-LNC
999			_c103844 _d103844