The version 2.96 of framework and GUI tool NeurophStudio has been released!

Neuroph 2.96 comes with various API improvements, several new features, more examples for standard introductory machine learning tasks, and start of initial integration of Java Visual Recognition API JSR381.

API improvements are related to normalization methods, data set spliting/sampling, and kfold crossvalidation. There are also inital implementations of new neural network architectures like ART and LSTM.and HE weight initialisation.

Samples project provides examples of 10 standard machine learning problems in package org.neuroph.samples.standard10ml which are suitable for teaching neural networks and machine learning basics with Java.

This release also includes initial implementation of some parts of Visual Recognition API, which is being developed as official Java technology Standard for Visual Recognition tasks within JCP (Java Community Process).

GitHub repository has also been reorganized so the framework and NeurophStudio GUI are now separate repositories.
https://github.com/neuroph/NeurophFramework
https://github.com/neuroph/NeurophStudio

With develpoment of a number of advanced deep learning framework with support for GPU and distributed computing, such as Tensorflow, PyTorch, Deeplearning4j and many others, the main purporse Of Neuroph is to be an entry level and educational software to introduce Java developers to field of neural networks and deep learning. It is also handy solution for application for smaller scale problems, which doesn't require huge ammount of data.

At some universities it is being used as first step to learn basic concepts and principles before moving to more advanced frameworks, thanks to interactive examples, visualizations, readable source code and user friendly IDE-like environment.

We're working on Neural Newtorks ExPLODE teaching methodology (EXploratory Programming and Learning Open Developemnt Environment) which will provide set of tools, tutorials and teaching methodology that will be the fastest way for Java developers to learn neural networks/machine learning.

For existing Neuroph users who might need more advanced features and additional support we recommend to take a look at Deep Netts Platform https://deepnetts.com
Deep Netts Platform is built on same philosophy as Neuroph to create intuitive easy-to-use neural network/deep learning environment for software developers, and more advanced features and implementation. The future releases of Neuroph will include deep netts community edition, in order to provide better support for convolutional networks for image recognition.

And at the end, we want to announce the next release that's allready cooking, that will be based on Apache NetBeans 11, include support for Deep Netts Community Edition, and generic standard Java machine learning and visual recognition API which is being developed within JSR 381.

Many thanks to students from University of Belgrade, and other contributors and users all around the world who helped to build this release. Note that if some of the contibutions are not present in this release we'll integrate them in some of the next releases.

DOWNLOADS

Downloads for this release are available at our downloads page