https://github.com/pmuens/lab

Research Environment to play around with Algorithms and Data (Structures)
https://github.com/pmuens/lab

algorithms artificial-intelligence artificial-neural-networks data-science deep-learning jupyter jupyter-notebook machine-learning machine-learning-algorithms

Last synced: 16 days ago
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Research Environment to play around with Algorithms and Data (Structures)

• Host: GitHub
• URL: https://github.com/pmuens/lab
• Owner: pmuens
• Created: 2019-04-17T08:42:45.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-13T07:21:51.000Z (over 4 years ago)
• Last Synced: 2024-10-20T04:44:41.128Z (3 months ago)
• Topics: algorithms, artificial-intelligence, artificial-neural-networks, data-science, deep-learning, jupyter, jupyter-notebook, machine-learning, machine-learning-algorithms
• Language: Jupyter Notebook
• Homepage: https://philippmuens.com
• Size: 7.01 MB
• Stars: 54
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Lab

Personal lab to play around with algorithms and data.

**NOTE:** In order to make the implementations as understandable as possible I sometimes write more expressive code which could result in poor performance or disapproval of purists. I strongly believe that readability for such educational endeavors is more important than high-performance or idiomatic code.

## Implementations

### X from scratch

From scratch implementations of various algorithms and models in pure Python.

| Notebook                                      |               nbviewer               |           Google Colab            |            Blog post             |

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

| [Gradient Descent][gradient-descent-nb]       |  [Link][gradient-descent-nbviewer]   |  [Link][gradient-descent-colab]   |  [Link][gradient-descent-post]   |

| [k-NN][k-nn-nb]                               |        [Link][k-nn-nbviewer]         |        [Link][k-nn-colab]         |        [Link][k-nn-post]         |

| [Naive Bayes][naive-bayes-nb]                 |     [Link][naive-bayes-nbviewer]     |     [Link][naive-bayes-colab]     |     [Link][naive-bayes-post]     |

| [Linear Regression][linear-regression-nb]     |  [Link][linear-regression-nbviewer]  |  [Link][linear-regression-colab]  |  [Link][linear-regression-post]  |

| [Multiple Regression][multiple-regression-nb] | [Link][multiple-regression-nbviewer] | [Link][multiple-regression-colab] | [Link][multiple-regression-post] |

| [Logistic Regression][logistic-regression-nb] | [Link][logistic-regression-nbviewer] | [Link][logistic-regression-colab] | [Link][logistic-regression-post] |

| [Decision Trees][decision-trees-nb]           |   [Link][decision-trees-nbviewer]    |   [Link][decision-trees-colab]    |   [Link][decision-trees-post]    |

| [Neural Networks][neural-networks-nb]         |   [Link][neural-networks-nbviewer]   |   [Link][neural-networks-colab]   |           Coming soon            |

| [k-means Clustering][k-means-clustering-nb]   | [Link][k-means-clustering-nbviewer]  | [Link][k-means-clustering-colab]  |           Coming soon            |

[gradient-descent-nb]: ./x-from-scratch/gradient-descent-from-scratch.ipynb

[gradient-descent-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/gradient-descent-from-scratch.ipynb

[gradient-descent-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/gradient-descent-from-scratch.ipynb

[gradient-descent-post]: https://philippmuens.com/gradient-descent-from-scratch/

[k-nn-nb]: ./x-from-scratch/k-nn-from-scratch.ipynb

[k-nn-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/k-nn-from-scratch.ipynb

[k-nn-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/k-nn-from-scratch.ipynb

[k-nn-post]: https://philippmuens.com/k-nearest-neighbors-from-scratch/

[naive-bayes-nb]: ./x-from-scratch/naive-bayes-from-scratch.ipynb

[naive-bayes-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/naive-bayes-from-scratch.ipynb

[naive-bayes-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/naive-bayes-from-scratch.ipynb

[naive-bayes-post]: https://philippmuens.com/naive-bayes-from-scratch/

[linear-regression-nb]: ./x-from-scratch/linear-regression-from-scratch.ipynb

[linear-regression-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/linear-regression-from-scratch.ipynb

[linear-regression-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/linear-regression-from-scratch.ipynb

[linear-regression-post]: https://philippmuens.com/linear-and-multiple-regression-from-scratch/

[multiple-regression-nb]: ./x-from-scratch/multiple-regression-from-scratch.ipynb

[multiple-regression-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/multiple-regression-from-scratch.ipynb

[multiple-regression-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/multiple-regression-from-scratch.ipynb

[multiple-regression-post]: https://philippmuens.com/linear-and-multiple-regression-from-scratch/

[logistic-regression-nb]: ./x-from-scratch/logistic-regression-from-scratch.ipynb

[logistic-regression-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/logistic-regression-from-scratch.ipynb

[logistic-regression-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/logistic-regression-from-scratch.ipynb

[logistic-regression-post]: https://philippmuens.com/logistic-regression-from-scratch/

[decision-trees-nb]: ./x-from-scratch/decision-trees-from-scratch.ipynb

[decision-trees-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/decision-trees-from-scratch.ipynb

[decision-trees-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/decision-trees-from-scratch.ipynb

[decision-trees-post]: https://philippmuens.com/decision-trees-from-scratch/

[neural-networks-nb]: ./x-from-scratch/neural-networks-from-scratch.ipynb

[neural-networks-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/neural-networks-from-scratch.ipynb

[neural-networks-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/neural-networks-from-scratch.ipynb

[k-means-clustering-nb]: ./x-from-scratch/k-means-clustering-from-scratch.ipynb

[k-means-clustering-nbviewer]: https://nbviewer.jupyter.org/github/pmuens/lab/blob/master/x-from-scratch/k-means-clustering-from-scratch.ipynb

[k-means-clustering-colab]: https://colab.research.google.com/github/pmuens/lab/blob/master/x-from-scratch/k-means-clustering-from-scratch.ipynb

## Running it

**NOTE:** You can pass an optional port number as the first CLI argument (i.e. `./jupyter-lab 3000`).

### Jupyter Lab

```sh

./jupyter-lab.sh

```

### Jupyter Notebook

```sh

./jupyter-notebook.sh

```