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Neuroph

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6 reviews
  • 1 profiles
  • 1 categories
Average star rating
4.6
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Neuroph Reviews

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RM
Rohit M.
05/26/2023
Validated Reviewer
Review source: G2 invite
Incentivized Review

Great multi-layer neural network Java Framework

- Offer an IDE where one can smoothly configure their neural network. - Options to choose the learning algorithm of your choice.
Shringarika J.
SJ
Shringarika J.
Quality Control Officer | GLP Professional
02/19/2023
Validated Reviewer
Review source: G2 invite
Incentivized Review

Neuroph: Neural Nework Framework

1. It has the best Java library that too is open source. 2. Java library is well designed with small open classes. 3. Its user interface is easier to use. 4. Creation of Java neural network is quick.
Jeneen R.
JR
Jeneen R.
Health Assistant | COVID-19 Case Investigator | Contact Tracer | Outreach Specialist
10/18/2022
Validated Reviewer
Review source: G2 invite
Incentivized Review

Great Software so far!

I like how the software allows multiple people to work on it without encountering any glitches.

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What is Neuroph?

Neuroph, found on the DeepNetts website at https://www.deepnetts.com/, is an open-source Java neural network framework that simplifies the process of developing, training, and deploying neural networks. Suitable for both beginners and advanced users, Neuroph provides tools and functionalities to design neural network architectures through an intuitive interface. Its easy-to-use design is highly accessible for users who are not deeply specialized in machine learning, making it a popular choice for education and research.Neuroph supports various neural network types, including perceptrons, convolutional neural networks, and multi-layer perceptrons. Additionally, it integrates well with the broader Java ecosystem, offering a seamless experience for developers familiar with Java programming.DeepNetts, the host for Neuroph, provides additional professional tools and services tailored towards more comprehensive AI development and deployment solutions, thereby extending the capabilities offered by the Neuroph framework. This integration allows users to scale from simple experiments to complex, real-world AI applications.

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