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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

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Efficient BackProp
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    Chapter 3 Early Stopping - But When?
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    Chapter 4 A Simple Trick for Estimating the Weight Decay Parameter
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    Chapter 5 Controlling the hyperparameter search in MacKay’s Bayesian neural network framework
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    Chapter 6 Adaptive Regularization in Neural Network Modeling
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    Chapter 7 Large Ensemble Averaging
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    Chapter 8 Square Unit Augmented Radially Extended Multilayer Perceptrons
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    Chapter 9 A Dozen Tricks with Multitask Learning
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    Chapter 10 Solving the Ill-Conditioning in Neural Network Learning
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    Chapter 11 Centering Neural Network Gradient Factors
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    Chapter 12 Avoiding roundoff error in backpropagating derivatives
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    Chapter 13 Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation
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    Chapter 14 Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton
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    Chapter 15 Neural Network Classification and Prior Class Probabilities
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    Chapter 16 Applying Divide and Conquer to Large Scale Pattern Recognition Tasks
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    Chapter 17 Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions
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    Chapter 18 How to Train Neural Networks
Attention for Chapter 16: Applying Divide and Conquer to Large Scale Pattern Recognition Tasks
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