The second edition of the book "reloads" the first edition with more tricks

Provides a timely snapshot of tricks, theory and algorithms that are of use

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