https://github.com/krishnac7/classification

https://github.com/krishnac7/classification

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/krishnac7/classification
• Owner: krishnac7
• Created: 2017-06-24T16:03:18.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-06-24T16:20:24.000Z (over 7 years ago)
• Last Synced: 2024-04-27T19:34:37.724Z (7 months ago)
• Language: Jupyter Notebook
• Size: 1.66 MB
• Stars: 5
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

Awesome Lists containing this project

README

        
#Classification Deck

* Testnotebook contains the Complete version of Basic IRIS data classification along with performance evaluation and algorithm comparision

* Classification Simple consists of a shortened version of classifier with no evaluation

	* IRIS data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.

* MNIST Simple collects the required data itself when run for the first time and this project can be used to identify various handwritten digits and classify them into numerals between 0-9.

	* MNIST uses about 47000 images to train a Deep Belief network powered by Tensorflow running on DSX.On a regular computer, The training takes about 20 min to complete but DSX does it under less than a minute.

	* More Details on MNIST can be found [here](http://yann.lecun.com/exdb/mnist/)