Table of Contents

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   Book Overview
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   Chapter 1 Introduction
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   Chapter 2 Speeding Learning
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   Chapter 3 Efficient BackProp
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   Chapter 4 Regularization Techniques to Improve Generalization
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   Chapter 5 Early Stopping — But When?
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   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
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    Chapter 10 Improving Network Models and Algorithmic Tricks
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    Chapter 11 Square Unit Augmented, Radially Extended, Multilayer Perceptrons
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    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
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    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
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    Chapter 21 Tricks for Time Series
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    Chapter 22 Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions
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    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
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    Chapter 26 Practical Recommendations for Gradient-Based Training of Deep Architectures
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    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
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    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
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    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
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    Chapter 37 Forecasting with Recurrent Neural Networks: 12 Tricks
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    Chapter 38 Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks
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    Chapter 39 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers