New answers tagged

6

Unfortunately, even with large amounts of training data, hyperparameter choices can strongly influence the performance of a trained model. What you can usually drop when you have large amounts of training data is regularisation. If your training examples cover the function space you are learning really well, then it is harder to overfit the training data. ...


3

You don't NEED a hyperparameter tuner, but it can help in various situations. For example, if your model is not training well, perhaps using a tuner can help. It's hard to say in which hyperparameters you would be turning over in your specific model, but for some specific hyperparameters if you choose a bad value your model won't learn or diverge. Take for ...


0

The ideal hyperparameters is usually dependent on your dataset and will differ on a case by case basis. Go for trial and error to determine the hyperparameters that works best for you. Few research papers similar to your use case is listed below. CNN transfer learning for visual guitar chord classification A Study of Left Fingering Detection Using CNN for ...


Top 50 recent answers are included