Java Deep Learning Cookbook

How it works...

In step 1, k is the arbitrary number of choice and dataSet is the dataset object that represents your training data. We perform k-fold cross-validation to optimize the model evaluation process.

Complex neural network architectures can cause the network to tend to memorize patterns. Hence, your neural network will have a hard time generalizing unseen data. For example, it's better and more efficient to have a few hidden layers rather than hundreds of hidden layers. That's the relevance of step 2.

Fairly large training data will encourage the network to learn better and a batch-wise evaluation of test data will increase the generalization power of the network. That's the relevance of step 3. Although there are multiple types ...