UNI_Deep_learning | provide scripts for preprocessing dataset

by francisc0garcia Python Version: Current License: No License

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kandi X-RAY | UNI_Deep_learning Summary

UNI_Deep_learning is a Python library. UNI_Deep_learning has no bugs, it has no vulnerabilities and it has low support. However UNI_Deep_learning build file is not available. You can download it from GitHub.

We provide scripts for preprocessing dataset, training using tensor flow or torch and predictions. University of Kaiserslautern 2016.

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UNI_Deep_learning has a low active ecosystem.

It has 0 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

UNI_Deep_learning has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of UNI_Deep_learning is current.

Quality

UNI_Deep_learning has no bugs reported.

Security

UNI_Deep_learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

UNI_Deep_learning does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

UNI_Deep_learning releases are not available. You will need to build from source code and install.

UNI_Deep_learning has no build file. You will be need to create the build yourself to build the component from source.

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UNI_Deep_learning Key Features

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UNI_Deep_learning Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install UNI_Deep_learning

You can download it from GitHub.
You can use UNI_Deep_learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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