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MLPNeuralNet

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MLPNeuralNet

Overview

Synopsis

MLPNeuralNet is a fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples through trained neural networks. It is built on top of Apple's Accelerate Framework using vectored operations and hardware acceleration

Category

Artificial Neural Network Software

Features

•Works with iOS and Mac OS X
•Vectorised Implementation
•Works with double precision

License

Proprietary Software

Price

MLPNeuralNet is an Open Source program

Pricing

Subscription

Free Trial

Available

Users Size

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

Website

MLPNeuralNet

Company

MLPNeuralNet

What is best?

•Works with iOS and Mac OS X
•Vectorised Implementation
•Works with double precision

PAT Rating™

Editor Rating

Aggregated User Rating

Rate Here

Ease of use

8.5

5.1

Features & Functionality

8.7

7.9

Advanced Features

8.5

7.5

Integration

8.6

5.5

Performance

8.7

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Customer Support

7.6

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Implementation

—

Renew & Recommend

—

Bottom Line

MLPNeuralNet is designed to load and run models in forward propagation mode only. Some of the features that users will be able to take advantage of MLPNeuralNet would be Classification, Multi-class classification and regression output, Vectorised implementation, Works with double precision and Multiple hidden layers or none (in that case it's same as logistic/linear regression).

8.6

Editor Rating

6.5

Aggregated User Rating

4 ratings

You have rated this

MLPNeuralNet is for users who have engineered a prediction model using Matlab (Python or R) and would like to use it in an iOS application. In that case, MLPNeuralNet is exactly what is needed. MLPNeuralNet is designed to load and run models in forward propagation mode only.

Some of the features that users will be able to take advantage of MLPNeuralNet would be Classification, Multi-class classification and regression output, Vectorised implementation, Works with double precision and Multiple hidden layers or none (in that case it's same as logistic/linear regression). The program has extensive procedure and steps on how users can fully utilize the codes in order to use it with iOS and Mac OS X.

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