I’ve recently launched Homemade Machine Learning repository that contains examples of popular machine learning algorithms and approaches (like linear/logistic regressions, K-Means clustering, neural networks) implemented in Python with mathematics behind them being explained. Each algorithm has interactive Jupyter Notebook demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions right in your browser. In most cases the explanations are based on this great machine learning course by //medium.com/@andrewng">Andrew Ng.

The purpose of the repository was not to implement machine learning algorithms by using 3rd party library “one-liners” but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That’s why all algorithms implementations are called “homemade”.

The main Python libraries that are used there are NumPy and Pandas. These two are used for efficient matrix operations and for loading/parsing CSV datasets. When it comes to Jupyter Notebook demos then such libraries as Matplotlib and Plotly are being used for data visualizations.

Currently the following topics have been covered:

Solving a problem in real life is a lot like following a Disney script.

You have a hero, a conflict, failed attempts to solve the problem, back-up plans, a breakthrough, a solution that works and finally a conclusion.

By the end, we are back where we started, but transformed with new powers and knowledge.

Movies succeed with this narrative arc over and over again because we can all relate. If you look at Disney and Pixar movies, they are variations on this same timeless theme, sometimes called the hero’s journey.

 

In 1985, writer Christopher Vogler compiled the stages of the hero’s journey in a 7-page memo for Disney that became a guide for storytelling. That’s why it may seem so familiar.

But these stages don’t just work in Hollywood. They also work in real life.

Game theory is the study of mathematical models of strategic interaction in between rational decision-makers. It has applications in all fields of social science, as well as in logic and computer science. Originally, it addressed zero-sum games, in which each participant's gains or losses are exactly balanced by those of the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

Game types

Cooperative / non-cooperative