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        "author": "Nikesh Bajaj",
        "author_email": "nikkeshbajaj@gmail.com",
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        "description": "# Regularization for Machine Learning\n### These contents were taugh in summer school [**RegML 2016**](http://lcsl.mit.edu/courses/regml/regml2016/) by [Lorenzo Rosasco](http://web.mit.edu/lrosasco/www/) and this GUI in python was submitted as part of final exam.\n\n#### All the coded and tested functions are in [RegML.py](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML.py) and GUIs code structure is in [RegML_GUIv2.1.py](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML_GUIv2.1.py)\n\n## [Github Page](https://nikeshbajaj.github.io/Regularization_for_Machine_Learning/)\n## [PyPi -project](https://pypi.org/project/regml/)\n\n## Installation\n```\npip install regml\n```\n\n## Opening GUI:\n\n```\nimport regml\nregml.GUI()\n\n```\n\n#### Methods\n* Regularized Least Squares -RLS [Referance](https://en.wikipedia.org/wiki/Regularized_least_squares)\n* Nu-Method [Referance]()\n* Iterative Landweber Method [Referance](https://en.wikipedia.org/wiki/Landweber_iteration)\n* Singular Value Decomposition [Reference](https://en.wikipedia.org/wiki/Singular-value_decomposition)\n* Trunctated SVD [Referance 1](http://arxiv.org/pdf/0909.4061) [Referance 2](http://langvillea.people.cofc.edu/DISSECTION-LAB/Emmie%27sLSI-SVDModule/p5module.html)\n* Spectral cut-off\n\n#### Kernal Learning \n(Linear, Polynomial, Gaussian)\n* Linear ![equation1](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20X%5ETY)\n* Polynomial ![equation2](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20%28X%5ET%20Y%20+%201%29%5Ep)\n* Gaussian (RBF) ![equation3](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20exp%5E%7B-%5Cleft%20%5C%7C%20X-Y%20%5Cright%20%5C%7C%5E2%20/%202%5Csigma%20%5E2%7D)\n\n**K-Fold Cross Validation**\n\n## GUI\n\n\n# Using local files\n---\n## Use these files\n1. RegML.py\n2. RegML_GUIv2.1.py\n3. Getting_Started_Demo.ipynb\n\n## Requirments \n### Following libraries are required to use all the functions in RegML library\n1. Python(=2.7)\n2. Numpy(>=1.10.4)     [Numpy](https://pypi.python.org/pypi/numpy) \n3. Matplotlib(>=0.98)  [Matplotlib](https://github.com/matplotlib/matplotlib) \n4. Scipy(>=0.12)       Optional -(If you need to import .mat data files)  [Scipy](https://www.scipy.org/install.html) \n\n## Tested with following version\nGUI is tested on followwing version of libraries\n* Python     2.7, 3.7\n* Numpy      1.10.4\n* Matplotlib 1.15.1\n* Scipy      0.17.0\n\n## Getting starting with GUI\n\n### Windows------------------------\nAfter lauching python, go to directory containing RegML.py and RegML_GUIv2.1.py files and run following command on\npython shell\n```\n>> run RegML_GUIv2.1.py\n```\nIf you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run it\n\n### Ubuntu/Linux-------------------\n\nOpen terminal, cd to directory contaning all the files and execute following command\n```\n$ python RegML_GUIv2.1.py\n```\nif you have both python 2 and python 3\n\n```\n$ python2 RegML_GUIv2.1.py\n```\n\nIf you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run it\n\n\n## Getting Started with DEMO\nGetting_Started_Demo is a IPython -Notebook, which can be open in Ipython-Notebook or Jupyter\n\n# [**Notebook**](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/Getting_Started_Demo.ipynb)\n\n\n# [**RegML Library**](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML.py)\n\n______________________\n\n### Nikesh Bajaj\n\nn.bajaj@qmul.ac.uk\n\nnikesh.bajaj@elios.unige.it\n\n[http://nikeshbajaj.in](http://nikeshbajaj.in)\n\n\n",
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