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Here is implementation of Neural Network from scratch without using any libraries of ML Only numpy is used for NN and matplotlib for plotting the results visulization of deep layers are also shown in the examples

Optimization: Gradient Decent -Basic one, Momentum, RMSprop, Adam (RMS+ Momentum)

Regularization: L2 Penalization, Dropouts

Data sets: Two class dataset : Gaussian, Linear, Moons, Spiral, Sinasodal, Multiclass: gaussian distribuated data upto 9 classes

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Methods

Code: Python GUI, IPython-Notebook

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Kernal Learning

  • Linear         equation1
  • Polynomail      equation2
  • Gaussian (RBF)  equation3

Github Page Github Ropo

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Code available on PyPI libraries
Installation:

pip install regml

Execute:

import regml
regml.GUI()

PyPI

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Linear Feedback Shift Register

LFSR code with Python and Matlab both, can be used for modeling A5/1 Generator or Implementing Enhancement of A5/1 as per [https://doi.org/10.1109/ETNCC.2011.5958486]

Code : Python & Matlab

Github Page | Github Ropo

Python Code is available on PyPI PyPI

 Installation:

pip install pylfsr

Documentation