Repositories - Nikesh Bajaj

Python Libraries : PyPI

SpKit : spkit : pip install spkit

Signal Processing toolkit library includes, information theory based evaluation of signal, transforms techniques, filtering, some of the statistical operations, and many other analytical tools, not widely available in other pakackes
Documentation | Homepage
PhyAAt : phyaat : pip install phyaat

Python library for downloading the dataset, feature extraction, preprocessing and predictive modeling for PhyAAt project. | Homepage
PyLFSR : pylfsr : pip install pylfsr

Python library to generate pseudo random bit stream using Linear Feedback Shift Register. It also includes the tests of LFSR for three properties and A5/1 and Geffe Generators. Documentation. Homepage
RegML : regml : pip install regml

Python implementation for Regularization methods for Machine Learning with GUI, includes kernel learning and and regularizations techniques. | Homepage
MLEnd : mlend : pip install mlend

Python library for downloading and beachmarking for MLEnd Datasets | Homepage

Deep Learning from Scratch (without any ML libraries)

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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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Decision Trees from Scratch (without any ML libraries)

Github Repo| Jupyter-notebook

Spkit

Regularization methods for machine learning Kernel Learning & regularization

Methods

Regularized Least Squares -RLS Referance
Nu-Method Referance
Iterative Landweber Method Referance
Singular Value Decomposition Reference
Trunctated SVD Referance 1 Referance 2
Spectral cut-off

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 pip install pylfsr

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]