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Neural Networks: Tricks of the Trade

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Cover of 'Neural Networks: Tricks of the Trade'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction
  3. Altmetric Badge
    Chapter 2 Speeding Learning
  4. Altmetric Badge
    Chapter 3 Efficient BackProp
  5. Altmetric Badge
    Chapter 4 Regularization Techniques to Improve Generalization
  6. Altmetric Badge
    Chapter 5 Early Stopping — But When?
  7. Altmetric Badge
    Chapter 6 A Simple Trick for Estimating the Weight Decay Parameter
  8. Altmetric Badge
    Chapter 7 Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework
  9. Altmetric Badge
    Chapter 8 Adaptive Regularization in Neural Network Modeling
  10. Altmetric Badge
    Chapter 9 Large Ensemble Averaging
  11. Altmetric Badge
    Chapter 10 Improving Network Models and Algorithmic Tricks
  12. Altmetric Badge
    Chapter 11 Square Unit Augmented, Radially Extended, Multilayer Perceptrons
  13. Altmetric Badge
    Chapter 12 A Dozen Tricks with Multitask Learning
  14. Altmetric Badge
    Chapter 13 Solving the Ill-Conditioning in Neural Network Learning
  15. Altmetric Badge
    Chapter 14 Centering Neural Network Gradient Factors
  16. Altmetric Badge
    Chapter 15 Avoiding Roundoff Error in Backpropagating Derivatives
  17. Altmetric Badge
    Chapter 16 Representing and Incorporating Prior Knowledge in Neural Network Training
  18. Altmetric Badge
    Chapter 17 Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation
  19. Altmetric Badge
    Chapter 18 Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton
  20. Altmetric Badge
    Chapter 19 Neural Network Classification and Prior Class Probabilities
  21. Altmetric Badge
    Chapter 20 Applying Divide and Conquer to Large Scale Pattern Recognition Tasks
  22. Altmetric Badge
    Chapter 21 Tricks for Time Series
  23. Altmetric Badge
    Chapter 22 Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions
  24. Altmetric Badge
    Chapter 23 How to Train Neural Networks
  25. Altmetric Badge
    Chapter 24 Big Learning and Deep Neural Networks
  26. Altmetric Badge
    Chapter 25 Stochastic Gradient Descent Tricks
  27. Altmetric Badge
    Chapter 26 Practical Recommendations for Gradient-Based Training of Deep Architectures
  28. Altmetric Badge
    Chapter 27 Training Deep and Recurrent Networks with Hessian-Free Optimization
  29. Altmetric Badge
    Chapter 28 Implementing Neural Networks Efficiently
  30. Altmetric Badge
    Chapter 29 Better Representations: Invariant, Disentangled and Reusable
  31. Altmetric Badge
    Chapter 30 Learning Feature Representations with K-Means
  32. Altmetric Badge
    Chapter 31 Deep Big Multilayer Perceptrons for Digit Recognition
  33. Altmetric Badge
    Chapter 32 A Practical Guide to Training Restricted Boltzmann Machines
  34. Altmetric Badge
    Chapter 33 Learning Feature Hierarchies with Centered Deep Boltzmann Machines
  35. Altmetric Badge
    Chapter 34 Deep Learning via Semi-supervised Embedding
  36. Altmetric Badge
    Chapter 35 Identifying Dynamical Systems for Forecasting and Control
  37. Altmetric Badge
    Chapter 36 A Practical Guide to Applying Echo State Networks
  38. Altmetric Badge
    Chapter 37 Forecasting with Recurrent Neural Networks: 12 Tricks
  39. Altmetric Badge
    Chapter 38 Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks
  40. Altmetric Badge
    Chapter 39 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers
Attention for Chapter 26: Practical Recommendations for Gradient-Based Training of Deep Architectures
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

patent
2 patents
wikipedia
2 Wikipedia pages

Citations

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382 Dimensions

Readers on

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3978 Mendeley
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Chapter title
Practical Recommendations for Gradient-Based Training of Deep Architectures
Chapter number 26
Book title
Neural Networks: Tricks of the Trade
Published in
Lecture notes in computer science, January 2012
DOI 10.1007/978-3-642-35289-8_26
Book ISBNs
978-3-64-235288-1, 978-3-64-235289-8
Authors

Yoshua Bengio, Bengio, Yoshua

Editors

Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,978 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 26 <1%
Japan 15 <1%
Germany 12 <1%
China 12 <1%
United Kingdom 11 <1%
France 6 <1%
Australia 6 <1%
Canada 5 <1%
Colombia 4 <1%
Other 50 1%
Unknown 3831 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 918 23%
Student > Master 891 22%
Researcher 530 13%
Student > Bachelor 408 10%
Other 180 5%
Other 448 11%
Unknown 603 15%
Readers by discipline Count As %
Computer Science 1871 47%
Engineering 682 17%
Physics and Astronomy 122 3%
Mathematics 92 2%
Agricultural and Biological Sciences 83 2%
Other 404 10%
Unknown 724 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2023.
All research outputs
#3,419,366
of 23,567,572 outputs
Outputs from Lecture notes in computer science
#761
of 8,151 outputs
Outputs of similar age
#27,742
of 247,622 outputs
Outputs of similar age from Lecture notes in computer science
#53
of 489 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,151 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 247,622 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 489 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.