Neural Networks: Tricks of the Trade / edited by Grégoire Montavon, Geneviève Orr, Klaus-Robert Müller.

Format:

Book

Contributor:

Montavon, Grégoire, Editor.

Orr, Genevieve, Editor.

Müller, Klaus-Robert, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 7700

Theoretical Computer Science and General Issues, 2512-2029 ; 7700

Language:

English

Subjects (All):

Computer science.

Artificial intelligence.

Algorithms.

Pattern recognition systems.

Dynamics.

Nonlinear theories.

Application software.

Theory of Computation.

Artificial Intelligence.

Automated Pattern Recognition.

Applied Dynamical Systems.

Computer and Information Systems Applications.

Local Subjects:

Theory of Computation.

Artificial Intelligence.

Algorithms.

Automated Pattern Recognition.

Applied Dynamical Systems.

Computer and Information Systems Applications.

Physical Description:

1 online resource (XII, 769 pages) : 223 illustrations

Edition:

2nd ed. 2012.

Contained In:

Springer Nature eBook

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-642-35289-8

9783642352898

Access Restriction:

Restricted for use by site license.