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.
From scratch implementations of various algorithms and models in pure Python.
| Notebook | nbviewer | Google Colab | Blog post |
|---|---|---|---|
| Gradient Descent | Link | Link | Link |
| k-NN | Link | Link | Link |
| Naive Bayes | Link | Link | Link |
| Linear Regression | Link | Link | Link |
| Multiple Regression | Link | Link | Link |
| Logistic Regression | Link | Link | Link |
| Decision Trees | Link | Link | Link |
| Neural Networks | Link | Link | Coming soon |
| k-means Clustering | Link | Link | Coming soon |
NOTE: You can pass an optional port number as the first CLI argument (i.e. ./jupyter-lab 3000).
./jupyter-lab.sh./jupyter-notebook.sh