Supported neural network architectures •Adaline •Perceptron •Multi Layer Perceptron with Backpropagation, Momentum on Resilient Propagation •Hopfield network •Bidirectional Associative Memory •Kohonen network •Hebbian network •Maxnet •Competitive network •Instar •Outstar •RBF network •Neuro Fuzzy Reasoner Other features •Small number of the essential base classes in core package (only 10) which can be easily reused or extended •Support for supervised and unsupervised learning rules •An easy-to-follow structure and logic. •Java & Neural Network IDE, Neuroph Studio, based on NetBeans Platform •Image recognition support •OCR support •Stock market prediction sample •Learning vizualisation samples •Data normalization •Simple microbenchmarking framework

Classification: Identifying to which category an object belongs to. Regression: Predicting a continuous-valued attribute associated with an object. Clustering: Automatic grouping of similar objects into sets. Dimensionality Reduction: Reducing the number of random variables to consider. Model Selection: Comparing, validating and choosing parameters and models. Preprocessing: Feature extraction and normalization.