Ebook: Neural Networks: Tricks of the Trade: Second Edition

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.

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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.

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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.

Content:
Front Matter....Pages -
Introduction....Pages 1-5
Speeding Learning....Pages 7-8
Efficient BackProp....Pages 9-48
Regularization Techniques to Improve Generalization....Pages 49-51
Early Stopping — But When?....Pages 53-67
A Simple Trick for Estimating the Weight Decay Parameter....Pages 69-89
Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework....Pages 91-110
Adaptive Regularization in Neural Network Modeling....Pages 111-130
Large Ensemble Averaging....Pages 131-137
Improving Network Models and Algorithmic Tricks....Pages 139-141
Square Unit Augmented, Radially Extended, Multilayer Perceptrons....Pages 143-161
A Dozen Tricks with Multitask Learning....Pages 163-189
Solving the Ill-Conditioning in Neural Network Learning....Pages 191-203
Centering Neural Network Gradient Factors....Pages 205-223
Avoiding Roundoff Error in Backpropagating Derivatives....Pages 225-230
Representing and Incorporating Prior Knowledge in Neural Network Training....Pages 231-233
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation....Pages 235-269
Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton....Pages 271-293
Neural Network Classification and Prior Class Probabilities....Pages 295-309
Applying Divide and Conquer to Large Scale Pattern Recognition Tasks....Pages 311-338
Tricks for Time Series....Pages 339-341
Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions....Pages 343-367
How to Train Neural Networks....Pages 369-418
Big Learning and Deep Neural Networks....Pages 419-420
Stochastic Gradient Descent Tricks....Pages 421-436
Practical Recommendations for Gradient-Based Training of Deep Architectures....Pages 437-478
Training Deep and Recurrent Networks with Hessian-Free Optimization....Pages 479-535
Implementing Neural Networks Efficiently....Pages 537-557
Better Representations: Invariant, Disentangled and Reusable....Pages 559-560
Learning Feature Representations with K-Means....Pages 561-580
Deep Big Multilayer Perceptrons for Digit Recognition....Pages 581-598
A Practical Guide to Training Restricted Boltzmann Machines....Pages 599-619
Deep Boltzmann Machines and the Centering Trick....Pages 621-637
Deep Learning via Semi-supervised Embedding....Pages 639-655
Identifying Dynamical Systems for Forecasting and Control....Pages 657-658
A Practical Guide to Applying Echo State Networks....Pages 659-686
Forecasting with Recurrent Neural Networks: 12 Tricks....Pages 687-707
Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks....Pages 709-733
10 Steps and Some Tricks to Set up Neural Reinforcement Controllers....Pages 735-757
Back Matter....Pages -

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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.

Content:
Front Matter....Pages -
Introduction....Pages 1-5
Speeding Learning....Pages 7-8
Efficient BackProp....Pages 9-48
Regularization Techniques to Improve Generalization....Pages 49-51
Early Stopping — But When?....Pages 53-67
A Simple Trick for Estimating the Weight Decay Parameter....Pages 69-89
Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework....Pages 91-110
Adaptive Regularization in Neural Network Modeling....Pages 111-130
Large Ensemble Averaging....Pages 131-137
Improving Network Models and Algorithmic Tricks....Pages 139-141
Square Unit Augmented, Radially Extended, Multilayer Perceptrons....Pages 143-161
A Dozen Tricks with Multitask Learning....Pages 163-189
Solving the Ill-Conditioning in Neural Network Learning....Pages 191-203
Centering Neural Network Gradient Factors....Pages 205-223
Avoiding Roundoff Error in Backpropagating Derivatives....Pages 225-230
Representing and Incorporating Prior Knowledge in Neural Network Training....Pages 231-233
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation....Pages 235-269
Combining Neural Networks and Context-Driven Search for On-line, Printed Handwriting Recognition in the Newton....Pages 271-293
Neural Network Classification and Prior Class Probabilities....Pages 295-309
Applying Divide and Conquer to Large Scale Pattern Recognition Tasks....Pages 311-338
Tricks for Time Series....Pages 339-341
Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions....Pages 343-367
How to Train Neural Networks....Pages 369-418
Big Learning and Deep Neural Networks....Pages 419-420
Stochastic Gradient Descent Tricks....Pages 421-436
Practical Recommendations for Gradient-Based Training of Deep Architectures....Pages 437-478
Training Deep and Recurrent Networks with Hessian-Free Optimization....Pages 479-535
Implementing Neural Networks Efficiently....Pages 537-557
Better Representations: Invariant, Disentangled and Reusable....Pages 559-560
Learning Feature Representations with K-Means....Pages 561-580
Deep Big Multilayer Perceptrons for Digit Recognition....Pages 581-598
A Practical Guide to Training Restricted Boltzmann Machines....Pages 599-619
Deep Boltzmann Machines and the Centering Trick....Pages 621-637
Deep Learning via Semi-supervised Embedding....Pages 639-655
Identifying Dynamical Systems for Forecasting and Control....Pages 657-658
A Practical Guide to Applying Echo State Networks....Pages 659-686
Forecasting with Recurrent Neural Networks: 12 Tricks....Pages 687-707
Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks....Pages 709-733
10 Steps and Some Tricks to Set up Neural Reinforcement Controllers....Pages 735-757
Back Matter....Pages -
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