Neural Networks: Tricks of the Trade: Second Edition by Klaus-Robert Müller (auth.), Grégoire Montavon, Geneviève B.

By Klaus-Robert Müller (auth.), Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller (eds.)

The twenty final years were marked via a rise in to be had info and computing energy. In parallel to this development, the point of interest of neural community study and the perform of teaching neural networks has gone through a few very important adjustments, for instance, use of deep studying machines.

The moment variation of the ebook augments the 1st version with extra methods, that have resulted from 14 years of concept and experimentation by means of a number of the world's such a lot well known neural community researchers. those tips could make a considerable distinction (in phrases of pace, ease of implementation, and accuracy) by way of placing algorithms to paintings on actual problems.

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Inputs that have a large variation in spread along different directions of the input space will have a large condition number and slow learning. And so we recommend: Normalize the variances of the input variables. If the input variables are correlated, this will not make the error surface spherical, but it will possibly reduce its eccentricity. 7b) thus weight updates are not decoupled. Decoupled weights make the “one learning rate per weight” method optimal, thus, we have the following trick: Decorrelate the input variables.

2. : Optimal brain damage. S. ) Advances in Neural Information Processing Systems, vol. 2, pp. : Second order properties of error surfaces. In: Advances in Neural Information Processing Systems, vol. 3. : Automatic learning rate maximization by on-line estimation of the hessian’s eigenvectors. ) Advances in Neural Information Processing Systems, vol. 5. : A scaled conjugate gradient algorithm for fast supervised learning. : Supervised learning on large redundant training sets. A. LeCun et al. : Fast learning in networks of locally-tuned processing units.

G. after every fifth epoch. 3. Stop training as soon as the error on the validation set is higher than it was the last time it was checked. 4. Use the weights the network had in that previous step as the result of the training run. This approach uses the validation set to anticipate the behavior in real use (or on a test set), assuming that the error on both will be similar: The validation error is used as an estimate of the generalization error. 2. Early Stopping — But When? 2. 4 for a rough explanation of this behavior.

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