Metadata-Version: 2.1
Name: mnist-classifier
Version: 1.0.3
Summary: Basic mnist classifier example of a Reproducible Research Project in Python
Home-page: https://github.com/sandrich/classifying_digits_mnist
Author: Christian Sandrini
Author-email: mail@chrissandrini.ch
License: MIT
Description: ![CircleCI](https://img.shields.io/circleci/build/github/sandrich/classifying_digits_mnist/master)
        [![Coverage Status](https://coveralls.io/repos/github/sandrich/classifying_digits_mnist/badge.svg?branch=master)](https://coveralls.io/github/sandrich/classifying_digits_mnist?branch=master)
        [![Generic badge](https://img.shields.io/badge/doc-latest-orange.svg)](https://sandrich.github.io/classifying_digits_mnist/)
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        # M-NIST classification algorithm comparison
        
        ## Installation
        
        You can just use
        ```bash
        pip install mnist-classifier
        ```
        
        ## Documentation
        
        You can find all the information you need on the [documentation page](https://sandrich.github.io/classifying_digits_mnist/index.html)
        
        ## Motivation for project
        
        This project was realised in the scope of a course in Artificial Intelligence offered by [UniDistance](https://distanceuniversity.ch/artificial-intelligence/) and the [Idiap research Institute](https://github.com/idiap)
        
        The hypothesis motivating the development of this package is the following:
        
         > Random Forests can give similar resulting prediction models to MLP Neural Networks on the M-NIST digit dataset in significantly less time.
        
        With the code in this repository, we show that indeed, Random Forests *can* in fact produce similar (if not better) results with training times orders of magnitude smaller.
        
        ## License
        MIT
        
        ## Authors
        @sandrich - Christian Sandrini
        @bigskapinsky - Calixte Mayoraz
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
