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Neural Networks: Tricks of the Trade

Overview of attention for book
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
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    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
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    Chapter 7 Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework
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    Chapter 8 Adaptive Regularization in Neural Network Modeling
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    Chapter 9 Large Ensemble Averaging
  11. Altmetric Badge
    Chapter 10 Improving Network Models and Algorithmic Tricks
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    Chapter 11 Square Unit Augmented, Radially Extended, Multilayer Perceptrons
  13. Altmetric Badge
    Chapter 12 A Dozen Tricks with Multitask Learning
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    Chapter 13 Solving the Ill-Conditioning in Neural Network Learning
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    Chapter 14 Centering Neural Network Gradient Factors
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    Chapter 15 Avoiding Roundoff Error in Backpropagating Derivatives
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    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
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    Chapter 18 Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton
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    Chapter 19 Neural Network Classification and Prior Class Probabilities
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    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
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    Chapter 24 Big Learning and Deep Neural Networks
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    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
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    Chapter 28 Implementing Neural Networks Efficiently
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    Chapter 29 Better Representations: Invariant, Disentangled and Reusable
  31. Altmetric Badge
    Chapter 30 Learning Feature Representations with K-Means
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    Chapter 31 Deep Big Multilayer Perceptrons for Digit Recognition
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    Chapter 32 A Practical Guide to Training Restricted Boltzmann Machines
  34. Altmetric Badge
    Chapter 33 Learning Feature Hierarchies with Centered Deep Boltzmann Machines
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    Chapter 34 Deep Learning via Semi-supervised Embedding
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    Chapter 35 Identifying Dynamical Systems for Forecasting and Control
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    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 3: Efficient BackProp
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

6 X users
6 patents


382 Dimensions

Readers on

3523 Mendeley
6 CiteULike
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Chapter title
Efficient BackProp
Chapter number 3
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_3
Book ISBNs
978-3-64-235288-1, 978-3-64-235289-8

Yann A. LeCun, Léon Bottou, Genevieve B. Orr, Klaus-Robert Müller, LeCun, Yann A., Bottou, Léon, Orr, Genevieve B., Müller, Klaus-Robert


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

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,523 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 34 <1%
Germany 22 <1%
France 12 <1%
China 9 <1%
United Kingdom 8 <1%
Canada 8 <1%
Switzerland 6 <1%
Australia 4 <1%
Russia 4 <1%
Other 37 1%
Unknown 3379 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 870 25%
Student > Master 792 22%
Researcher 458 13%
Student > Bachelor 382 11%
Other 182 5%
Other 369 10%
Unknown 470 13%
Readers by discipline Count As %
Computer Science 1620 46%
Engineering 695 20%
Physics and Astronomy 112 3%
Mathematics 109 3%
Agricultural and Biological Sciences 65 2%
Other 358 10%
Unknown 564 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 06 April 2023.
All research outputs
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Outputs from Lecture notes in computer science
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Outputs of similar age from Lecture notes in computer science
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Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,229 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 90% 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 255,923 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 89% of its contemporaries.
We're also able to compare this research output to 487 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.