Neural networks : tricks of the trade / Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller (eds.)
Saved in:
| Online Access: |
Full Text (via Springer) |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
2012.
|
| Edition: | 2nd ed. |
| Series: | Lecture notes in computer science ;
7700. LNCS sublibrary. Theoretical computer science and general issues. |
| Subjects: |
MARC
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| 245 | 0 | 0 | |a Neural networks : |b tricks of the trade / |c Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller (eds.) |
| 250 | |a 2nd ed. | ||
| 260 | |a Berlin ; |a New York : |b Springer, |c 2012. | ||
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a Lecture notes in computer science ; |v 7700. | |
| 490 | 1 | |a LNCS sublibrary. SL 1, Theoretical computer science and general issues. | |
| 505 | 0 | 0 | |g Introduction / |r Klaus-Robert Müller -- |t Speeding Learning / |r Klaus-Robert Müller -- |t Efficient BackProp / |r Yann A. LeCun, Léon Bottou, Genevieve B. Orr and Klaus-Robert Müller -- |t Regularization Techniques to Improve Generalization / |r Klaus-Robert Müller -- |t Early Stopping -- But When? / |r Lutz Prechelt --A |t Simple Trick for Estimating the Weight Decay Parameter / |r Thorsteinn S. Rögnvaldsson -- |t Controlling the Hyperparameter Search in MacKay's Bayesian Neural Network Framework / |r Tony Plate -- |t Adaptive Regularization in Neural Network Modeling / |r Jan Larsen, Claus Svarer, Lars Nonboe Andersen and Lars Kai Hansen -- |t Large Ensemble Averaging / |r David Horn, Ury Naftaly and Nathan Intrator -- |t Improving Network Models and Algorithmic Tricks / |r Klaus-Robert Müller -- |t Square Unit Augmented, Radially Extended, Multilayer Perceptrons / |r Gary William Flake --A |t Dozen Tricks with Multitask Learning / |r Rich Caruana -- |t Solving the Ill-Conditioning in Neural Network Learning / |r Patrick van der Smagt and Gerd Hirzinger -- |t Centering Neural Network Gradient Factors / |r Nicol N. Schraudolph -- |t Avoiding Roundoff Error in Backpropagating Derivatives / |r Tony Plate. |
| 505 | 8 | 0 | |t Representing and Incorporating Prior Knowledge in Neural Network Training / |r Klaus-Robert Müller -- |t Transformation Invariance in Pattern Recognition -- Tangent Distance and Tangent Propagation / |r Patrice Y. Simard, Yann A. LeCun, John S. Denker and Bernard Victorri -- |t Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton / |r Larry S. Yaeger, Brandyn J. Webb and Richard F. Lyon -- |t Neural Network Classification and Prior Class Probabilities / |r Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi and C. Lee Giles -- |t Applying Divide and Conquer to Large Scale Pattern Recognition Tasks / |r Jürgen Fritsch and Michael Finke -- |t Tricks for Time Series / |r Klaus-Robert Müller -- |t Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions / |r John Moody -- |t How to Train Neural Networks / |r Ralph Neuneier and Hans Georg Zimmermann -- |t Big Learning and Deep Neural Networks / |r Grégoire Montavon and Klaus-Robert Müller -- |t Stochastic Gradient Descent Tricks / |r Léon Bottou -- |t Practical Recommendations for Gradient-Based Training of Deep Architectures / |r Yoshua Bengio -- |t Training Deep and Recurrent Networks with Hessian-Free Optimization / |r James Martens and Ilya Sutskever. |
| 505 | 8 | 0 | |t Implementing Neural Networks Efficiently / |r Ronan Collobert, Koray Kavukcuoglu and Clément Farabet -- |t Better Representations: Invariant, Disentangled and Reusable / |r Grégoire Montavon and Klaus-Robert Müller -- |t Learning Feature Representations with K-Means / |r Adam Coates and Andrew Y. Ng -- |t Deep Big Multilayer Perceptrons for Digit Recognition / |r Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella and Jürgen Schmidhuber --A |t Practical Guide to Training Restricted Boltzmann Machines / |r Geoffrey E. Hinton -- |t Deep Boltzmann Machines and the Centering Trick / |r Grégoire Montavon and Klaus-Robert Müller -- |t Deep Learning via Semi-supervised Embedding / |r Jason Weston, Frédéric Ratle, Hossein Mobahi and Ronan Collobert -- |t Identifying Dynamical Systems for Forecasting and Control / |r Grégoire Montavon and Klaus-Robert Müller --A |t Practical Guide to Applying Echo State Networks / |r Mantas Lukoševičius -- |t Forecasting with Recurrent Neural Networks: 12 Tricks / |r Hans-Georg Zimmermann, Christoph Tietz and Ralph Grothmann -- |t Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks / |r Siegmund Duell, Steffen Udluft and Volkmar Sterzing -- |t 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers / |r Martin Riedmiller. |
| 504 | |a Includes bibliographical references and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260. | |
| 700 | 1 | |a Montavon, Grégoire. | |
| 700 | 1 | |a Orr, Geneviève B. | |
| 700 | 1 | |a Müller, Klaus-Robert. | |
| 776 | 0 | 8 | |i Print version: |t Neural networks. |d [S.l.] : Springer, 2012 |z 364235288X |w (OCoLC)816512509. |
| 830 | 0 | |a Lecture notes in computer science ; |v 7700. | |
| 830 | 0 | |a LNCS sublibrary. |n SL 1, |p Theoretical computer science and general issues. | |
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