Another resource - https://sigopt.com/, in the vein of Whetlab.
e.g. For NLP:
* https://aws.amazon.com/blogs/ai/fast-cnn-tuning-with-aws-gpu...
Disclaimer: they did share free trials with some of our students.
Re:Hacker's guide to Neural Networks (2012)
by stared on Saturday July 15, 2017 @09:47AM
(#14776591)
It looks interesting, and talk with these guys (including the author) on a conference (GPU Tech Conf in San Jose). However, for other much more transparent systems, I am kind of anxious to add a black box.
Especially as most of neural network tweaking go well beyond hyperparameter tweaking - one need to adjust architecture, make sensible cross-validation, good augmentation, etc.
Some pointers:
* http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/
* http://www.alexirpan.com/2017/06/27/hyperparam-spectral.html
At deepsense.io we are developing Neptune to facilitate that process (compare models, version-control them), here: https://go.neptune.deepsense.io (a public release in less that a month!).