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Neuroph
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Neuroph

Overview
Synopsis

Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts.

Category

Artificial Neural Network Software

Features

•Easy-to-follow Structure
•OCR Support
•Data Normalization
•Image Recognition Support
•Stock Market Prediction Sample

License

Proprietary Software

Price

Contact for Pricing

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

Neuroph

What is best?

•Easy-to-follow Structure
•OCR Support
•Data Normalization
•Image Recognition Support
•Stock Market Prediction Sample

PAT Rating™
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
9.4
6.4
Features & Functionality
9.6
6.6
Advanced Features
9.5
6.0
Integration
9.6
5.2
Performance
9.4
5.0
Customer Support
7.5
5.0
Implementation
6.4
Renew & Recommend
5.7
Bottom Line

Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.

9.5
Editor Rating
5.7
Aggregated User Rating
11 ratings
You have rated this

 Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components.

It has been released as open source under the Apache 2.0 license. Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks. Neuroph is best for beginners with neural networks, especially if the user just wanted to try how it works without going into complicated theory and implementation.

Use Neuroph to quickly research for a particular project. It is small, well documented, easy to use, and very flexible neural network framework. Some of the supported neural network architectures of Neuroph are 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 and Neuro Fuzzy Reasoner.

Other features from Neuroph includes 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 and Java & Neural Network IDE, Neuroph Studio, based on NetBeans Platform. It also features image recognition support, OCR support, stock market prediction sample, learning visualization samples, data normalization as well as simple microbenchmarking framework. Registering in the Neuroph community will have priority in support, feature requests, and will have opportunity to participate in closed and exclusive Neuroph events and activities.

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Ease of use
Features & Functionality
Advanced Features
Integration
Performance
Customer Support
Implementation
Renew & Recommend

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