MNIST-Hand-Digit-Recognition-Tensorflow-Basic | Begineer level code for MNIST Hand digit

by pshwetank Python Version: Current License: GPL-3.0

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kandi X-RAY | MNIST-Hand-Digit-Recognition-Tensorflow-Basic Summary

MNIST-Hand-Digit-Recognition-Tensorflow-Basic is a Python library. MNIST-Hand-Digit-Recognition-Tensorflow-Basic has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However MNIST-Hand-Digit-Recognition-Tensorflow-Basic build file is not available. You can download it from GitHub.

Begineer level code for MNIST Hand digit recognition problem.

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MNIST-Hand-Digit-Recognition-Tensorflow-Basic has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

MNIST-Hand-Digit-Recognition-Tensorflow-Basic has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MNIST-Hand-Digit-Recognition-Tensorflow-Basic is current.

Quality

MNIST-Hand-Digit-Recognition-Tensorflow-Basic has no bugs reported.

Security

MNIST-Hand-Digit-Recognition-Tensorflow-Basic has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MNIST-Hand-Digit-Recognition-Tensorflow-Basic is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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MNIST-Hand-Digit-Recognition-Tensorflow-Basic releases are not available. You will need to build from source code and install.

MNIST-Hand-Digit-Recognition-Tensorflow-Basic has no build file. You will be need to create the build yourself to build the component from source.

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MNIST-Hand-Digit-Recognition-Tensorflow-Basic Key Features

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MNIST-Hand-Digit-Recognition-Tensorflow-Basic Examples and Code Snippets

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Vulnerabilities

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Install MNIST-Hand-Digit-Recognition-Tensorflow-Basic

You can download it from GitHub.
You can use MNIST-Hand-Digit-Recognition-Tensorflow-Basic 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.

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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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