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taspinar

siml

Machine Learning algorithms implemented from scratch.

TutorialsML/AI fundamentalsJupyter Notebook
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Created 2016-10-07 · Updated 2026-10-14 · #17529 today
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taspinar/siml is ranked 17,529 with 535 stars.

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taspinar/siml is ranked 17,529 among these 8 peers; transitive-bullshit/chatgpt-plugin-ts is ranked 17,530.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJupyter Notebook
CategoryTutorials
SubcategoryML/AI fundamentals
Stars535
Forks269
Contributors2
Created date2016-10-07
Updated date2026-10-14
Stars in 1 day0
Stars in 7 days1
Rank17,529
Previous rank6,980
Rank change-10,549
7-day stars rank11,455
Previous 7-day stars rank11,517
7-day stars rank change62
Total stars rank18,368
Previous total stars rank18,316
Total stars rank change-52
CountryNetherlands
README summary

Synopsis This repository contains popular Machine Learning algorithms, which have been introduced in various blog posts ( Most of the algorithms are accompanied with blog-posts in which I try to explain the mathematics behind and the interpretation of these algorithms. But more importantly, Machine Learning is easy. But the academic literature or even (wikipedia-pages) is full with unnecessary complicated terminology, notation and formulae. This gives people the idea that these ML algorithms can only be understood with a full

understanding of advanced math and statistics. Stripped from all of these superfluous language we are left with simple maths which can be expressed in a few lines of code. Notebooks explaining the mathematics I have also provided some notebooks, explaining the mathematics of some Machine Learning algorithms. Linear Regression and Logistic Regression Naive Bayes Classification Perceptron Classification Classification with Scikit-Learn Machine Learning with Signal Analysis techniques Time-series forecasting with signal analysis techniques Notebooks explaining Machine Learning with the

Wavelet Transform Introduction to PyWavelets (for Wavelet Analysis Using Wavelets to Visualize the Scaleogram, time-axis and Fourier Transform Classification of signals using the Continuous Wavelet Transform and Convolutional Neural Networks Classification of ECG signals using the Discrete Wavelet Transform and Gradient Boosting Classification of signals using the Discrete Wavelet Transform and several classifiers Installation To install **siML**: or you can clone the repository and in the folder containing setup.

Frequently asked questions

How many stars and forks are recorded?

taspinar/siml has 535 stars and 269 forks.

What is the repository rank?

taspinar/siml is ranked 17,529.

What language and category are recorded?

taspinar/siml has 535 stars, uses Jupyter Notebook and belongs to Tutorials.

Which repository is closest in rank?

taspinar/siml is compared with transitive-bullshit/chatgpt-plugin-ts, ranked 17,530 with 535 stars.

README

Synopsis

This repository contains popular Machine Learning algorithms, which have been introduced in various blog posts (http://ataspinar.com). Most of the algorithms are accompanied with blog-posts in which I try to explain the mathematics behind and the interpretation of these algorithms.

Motivation

Machine Learning is fun! But more importantly, Machine Learning is easy. But the academic literature or even (wikipedia-pages) is full with unnecessary complicated terminology, notation and formulae. This gives people the idea that these ML algorithms can only be understood with a full understanding of advanced math and statistics. Stripped from all of these superfluous language we are left with simple maths which can be expressed in a few lines of code.

Notebooks explaining the mathematics

I have also provided some notebooks, explaining the mathematics of some Machine Learning algorithms.

Notebooks explaining Machine Learning with the Wavelet Transform

Installation

To install siML:

(sudo) pip install siml

or you can clone the repository and in the folder containing setup.py

python setup.py install

Code Example

TODO

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