Greetings

Knowledge is a paradox. The more one understand, the more one realises the vastness of his ignorance.

—Viktor (Arcane, Season 2)

You have stepped onto the stickiest page of this site.

Welcome to a hierarchical exploration of Mathematics and Computer Science. 𐃏 𐃏

The legend is as follows:

  • Archived.
  • Done; でも、 \(\exists\) room for extension / improvement.
  • Editing; code written, ideas fleshed out - prose needs to be reworked.
  • Refactor; initial code included, but contains bugs, is incorrect or needs better Design Patterns.
  • Nothing. All I have is the seedling of an idea.

Note: The unticked pages do not come with warranty. You may be mislead; \(\mathfrak{hic\,\,sunt\,\,dracones}\).

You may view the entire tag taxonomy here, and the author taxonomy here.

A North Star

Classical Computer Science

Data Structures & Algorithms

Programming:

Paradigms

Languages

Libraries

Computer Science Projects

Games

Raspberry Pi

For the longest time I kept these projects private, but now after a half decade I have settled on these 2 stable configurations

Software Engineering

Architecture & Design

Implementation

Testing

Artificial Intelligence

Ethics

Adversarial Searching

Constraint Satisfaction Problems

Machine Learning

I have thought about this ML hierarchy inasmuch as Aristotle thought about the phylums of flowers.

I am not a Data Scientist, but rather a Computer Scientist and Mathematician.

As such, my interests lie in theory giving rise to applications. Not vice-versa–applications giving rise to theory–which I believe retard the habit of generalisation and thus imagination.

Datasets

The following are all tags, but visiting them provides contextual / historical information on the dataset as well as back-links to the models which have solved these problems.

Furthermore, many of the tags point you to the Monolithic 10,000 hours AI, ML, DL repo. As such, the README therein provides useful information as well as a scoreboard that maintains my accuracies on the below datasets :D

Theory

Supervised Learning

Regression

Classification

These methods can be adapted for regression, but they are more well suited to classification.

Unsupervised Learning

Deep Learning

Natural Language Processing

Computer Vision

Reinforcement Learning

Mathematics

Computer Vision

  • PhD?

N-Bday Problems

The compiled PDFs can be found in the above linked heading.

The following links contain the source code and solution sets:

Typesetting

LaTeX

Textbook Solutions