E-book
Neural Networks: Tricks of the Trade [electronic resource] : Second Edition / edited by Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller.
Record details
- ISBN: 9783642352898
- Physical Description: XII, 769 p. 223 illus. online resource.
- Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : 2012.
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Electronic resources
Introduction | ||
| Preface on Speeding Learning | ||
| 1. Efficient BackProp | ||
| Preface on Regularization Techniques to Improve Generalization | ||
| 2. Early Stopping â But When? | ||
| 3. A Simple Trick for Estimating the Weight Decay Parameter | ||
| 4. Controlling the Hyperparameter Search in MacKayâs 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 âTangent 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.. | ||
| ^^ |
