1. Create ImagePreProcessingScaler for image normalization:

DataNormalization scaler = new ImagePreProcessingScaler(0,1);

2. Create a neural network configuration and add default hyperparameters:

MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().weightInit(WeightInit.DISTRIBUTION)
 .dist(new NormalDistribution(0.0, 0.01))
 .activation(Activation.RELU)
 .updater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 1e-2, 0.1, 100000), 0.9))
 .biasUpdater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 2e-2, 0.1, 100000), 0.9))
 .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) // normalize to prevent vanishing or exploding gradients
 .l2(l2RegularizationParam)
 .list();

3. Create convolution layers for a CNN using ConvolutionLayer: 

builder.layer(new ConvolutionLayer.Builder(11,11)
 .nIn(channels)
 .nOut(96)
 .stride(1,1)
 .activation(Activation.RELU)
 .build());

4. Configure subsampling layers using SubsamplingLayer: 

builder.layer(new SubsamplingLayer.Builder(PoolingType.MAX)
 .kernelSize(kernelSize,kernelSize)
 .build());

5. Normalize activation between layers using LocalResponseNormalization:

 builder.layer(1, new LocalResponseNormalization.Builder().name("lrn1").build());