Deep Learning from Scratch

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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Decesion Trees from scratch

Here is implementation of Decesion Trees from scratch. Only numpy is used for implemantation and matplotlib for plotting the results as shown in the examples

Classification and regression tree

Visualize tree while taining or after training is done

Print tree stucture

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Regularization for Machine Learning

Methods
Code: Python GUI, IPython-Notebook

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

Github Ropo

PyPi Page

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Installation:
pip install regml

Linear Feedback Shift Register

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 PyPi

Installation:
pip install pylfsr

Ensemble Approach for Classification and Regression

Ensemble Approaches for Classification and Regression

Download PPT  Visit Course page