Neural Networks Tricks of the Trade [Paperback]

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.

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 MacKays 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.- 1l“‘