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https://github.com/ovysotska/in_simple_english

Small projects to clarify big concepts
https://github.com/ovysotska/in_simple_english

easy-math math simple-english

Last synced: 2 months ago
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Small projects to clarify big concepts

Host: GitHub
URL: https://github.com/ovysotska/in_simple_english
Owner: ovysotska
Created: 2018-04-16T04:15:20.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2023-12-11T15:01:12.000Z (6 months ago)
Last Synced: 2024-03-21T13:56:36.106Z (3 months ago)
Topics: easy-math, math, simple-english
Language: Jupyter Notebook
Homepage:
Size: 11.5 MB
Stars: 232
Watchers: 13
Forks: 37
Open Issues: 2
Metadata Files:
- Readme: readme.md

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awesome-mobile-robotics - Small projects to clarify big concepts

README

        # Small projects to clarify big concepts

In this project, I try to clarify for myself and others the big mathematical (and not only) concepts.

I try to find the simplest possible example and roll from there by asking a lot of "simple/obvious/stupid" questions.

Here you can find a collection of Jupyter notebooks with different amount of content in them.

The links below will render the notebooks in [**nbviewer**](https://nbviewer.jupyter.org/).

## Main finished notebooks

|  |   | |

:-------------------------:|:-------------------------: | :-------------------------:

[![](data/intro/gradient_descent.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/gradient_descent.ipynb)   |     [![](data/intro/interpolation.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/Interpolation.ipynb) |  [![](data/intro/la_systems.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/system_of_linear_equations.ipynb)

[![](data/intro/bag_of_words.png)](bag_of_visual_words.ipynb)  |  [![](data/intro/local_image_operators.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/local_image_operators.ipynb)  |  [![](data/intro/tp_sorting.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/topological_sorting.ipynb)

[![](data/intro/kl_divergence.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/Kullback_Leibler.ipynb) |   [![](data/intro/ml_regression.png)](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/ml_regression.ipynb) |

[**gradient_descent**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/gradient_descent.ipynb) - simplistic visualization of 1D and 2D gradient descent.

[**bag_of_visual_words**](bag_of_visual_words.ipynb) - tf-idf reweighting for visual bag of words in pictures.

[**homogeneous_coords**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/homogeneous_coords.ipynb) - couple of geometric operation for homogeneous points.

[**Interpolation**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/Interpolation.ipynb) - mainly thoughts about cubic interpolation and how to apply interpolations for scaling up images.

[**system_of_linear_equations**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/system_of_linear_equations.ipynb) - overview of how to solve Ax=b and Ax=0

[**local_image_operators**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/local_image_operators.ipynb) - local image operators. Applying Binomial, Box and Sobel filter.

[**topological_sorting**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/topological_sorting.ipynb) - code snippet to practice graph search using topological sorting.

[**Kullback_Leibler**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/Kullback_Leibler.ipynb)  - an example of comparing two 1D discrete distribution using Kullback-Leibler divergence.

[**ml_regression**](https://nbviewer.jupyter.org/github/ovysotska/in_simple_english/blob/master/ml_regression.ipynb) - maximum likelihood estimation for linear regression. Bundle adjustment as a ML estimation method

## Folder `in_progress`

This folder contains more complicated topics which were not completely simplified yet.

## Gaussian Processes (gp)

	* **GP_starting example**  - implementing GP from scratch

	* **Gaussian_processes_functional** - GP implementation using funtional programming and multi dimensional input

	* **SkLearn_example** - model selection and first steps for optimal parameter selection using sklearn framework