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DN2A is a set of highly decoupled JavaScript modules for Neural Networks and Artificial Intelligence development. Each module is based on injection by configuration and you can use a single module alone, more of them together or just the complete set.
Category
Artificial Neural Network Software
Features
•Modularized components
•Configurable precision
•Configuration checker
•StepByStep training
•StepByGoal training
•Continuous training
License
Proprietary Software
Pricing
Subscription
Free Trial
Available
Users Size
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
Company
DN2A
What is best?
•Modularized components
•Configurable precision
•Configuration checker
•StepByStep training
•StepByGoal training
PAT Rating™
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
8.4
7.2
Features & Functionality
8.4
—
Advanced Features
8.6
—
Integration
8.5
—
Performance
8.6
10
Customer Support
7.4
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Implementation
—
Renew & Recommend
—
Bottom Line
DN2A main goal is to allow you to design, train and use without pain Single Neural Networks as well as very powerful Neural Networks Chains through which implement your Artificial Intelligence solution.
8.3
Editor Rating
8.6
Aggregated User Rating
1 rating
You have rated this
DN2A is a set of highly decoupled JavaScript modules for Neural Networks and Artificial Intelligence development. Each module is based on injection by configuration.
Clients can use a single module alone, more of them together or just the complete set. DN2A’s main goal is to allow users to design, train and use Single Neural Networks with ease as well as very powerful Neural Networks Chains through which to implement their Artificial Intelligence solutions.
DN2A’s side goals are to simplify integration, to speed up training/querying, to allow clustering and to represent the architecture and the relative data of each Neural Network as a (re)combinable string strain that will be usable within genetics optimization techniques. Some of DN2A’s highlights include Modularized Components which aid the development and the clear separation of concerns with great benefits for those who want to use mixed solutions.
Configurable Precision helps to avoid the noise deriving from operation errors and default system precision limits with great improvement of the learning speed and performance stability.
Configuration Checker helps to write fewer details about configuration and to keep compatibility with older versions while the project evolves. StepByStep Training allows users to train neural networks doing a single iteration over the passed information without trying to reach a specific parametric condition.
StepByGoal Training enables users to train neural networks doing a finite or infinite number of iterations over the passed information unless a specific parametric condition is reached. Finally, Continuous Training helps to train neural networks doing an infinite number of iterations over the passed information.
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By clicking Sign In with Social Media, you agree to let PAT RESEARCH store, use and/or disclose your Social Media profile and email address in accordance with the PAT RESEARCH Privacy Policy and agree to the Terms of Use.