Practical guide to hyperparameters search for deep learning models

Are you tired of babysitting your DL models? If so, you’re in the right place. In this post, we discuss motivations and strategies behind effectively searching for the best set of hyperparameters for any deep learning model. We’ll demonstrate how this can be done on FloydHub, as well as which direction the research is moving. When you’re done reading this post, you’ll have added some powerful new tools to your data science tool-belt – making the process of finding the best configuration for your deep learning task as automatic as possible. Unlike machine learning models, deep learning models are literally full of hyperparameters. Would you like some some evidence? Just take a look at the Transformer base v1 hyperparameters definition.I rest my case. Of course, not all of these variables contribute in the same way to the model’s learning process, but, given this additional complexity, it’s clear that finding the


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