Deep Learning from Scratch

 

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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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Regularization for Machine Learning

Methods
Code: Python GUI, IPython-Notebook

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

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Github Ropo

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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

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Ensemble Approach for Classification and Regression

Ensemble Approaches for Classification and Regression

Download PPT  Visit Course page