August 2018
Intermediate to advanced
378 pages
9h 9m
English
Content preview from R Deep Learning Essentials - Second Edition
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Tuning hyperparameters
All machine learning algorithms have hyper-parameters or settings that can change how they operate. These hyper-parameters can improve the accuracy of a model or reduce the training time. We have seen some of these hyper-parameters in previous chapters, particularly Chapter 3, Deep Learning Fundamentals, where we looked at the hyper-parameters that can be set in the mx.model.FeedForward.create function. The techniques in this section can help us find better values for the hyper-parameters.
Selecting hyper-parameters is not a magic bullet; if the raw data quality is poor or if there is not enough data to support training, then tuning hyper-parameters will only get you so far. In these cases, either acquiring additional ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.