Confirmed features and facts

Advantages of Omega Server neural network over classical neural networks

We created the Omega Server platform where any individual user or organization can experiment with the PANN neural network using their datasets; verify the unique qualities of the neural network, and solve their practical problems.

Confirmed features and facts
FeatureCompeting classical neural networks*
(Google, IBM, Miscrosoft)
PANN©
neural networks
Required number of network training epochs1,000,00010
Training speed (7 000 images)12,000 s. (3.3 hours)4 s.
Full task parallelization during network trainingNot possibleYes
Online up-training of an already trained network and removal of unnecessary dataNot possible.
Requires re-training
Yes
Network size (amount of inputs and outputs)Limited.
Exponential growth of training time
Unlimited.
Processing of images of any size
Increasing network size during computationNot possibleYes.
Networks can be expanded to any size

IBM SPSS Statistics 22
Competing classical neural networks (Google, IBM, Miscrosoft)

Distribution kit

Upload your data to the Omega Server neural network, test and see the capabilities of our neural network. Use your datasets to solve your practical problems. To get a link to the distribution kit just fill out this form. You will receive a link, as well as a manual, and will be able to use our network to work with your data and to publish the resulting neural network application.


    Thank you for your interest in Omega Server!
    You can now download the distribution kit.

    Please remember that you can use it ONLY AFTER you get an email confirming your registration on the server.

    Download

    GitHub

    As you know, neural network developers use various databases to test their networks. The instructions and data for testing Omega Server with IRIS and MNiST databases are available on GitHub.

    Geoffrey Hinton has figuratively described MNIST as the ‘Drosophila of machine learning,’ meaning that it allows machine learning researchers to study their algorithms under controlled laboratory conditions, similar to how biologists often study fruit flies.

    IRIS: https://github.com/Omega-Server/p-net-comparison-iris
    MNIST: https://github.com/Omega-Server/p-net-comparison

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