Scala Machine Learning Projects

Tuning and optimizing CNN hyperparameters

The following hyperparameters are very important and must be tuned to achieve optimized results.

• Dropout: Used for random omission of feature detectors to prevent overfitting
• Sparsity: Used to force activations of sparse/rare inputs
• Adagrad: Used for feature-specific learning-rate optimization
• Regularization: L1 and L2 regularization
• Weight transforms: Useful for deep autoencoders
• Probability distribution manipulation: Used for initial weight generation
• Gradient normalization and clipping

Another important question is: when do you want to add a max pooling layer rather than a convolutional layer with the same stride? A max pooling layer has no parameters at all, whereas a convolutional layer has ...