SHAP in other words (Shapley Additive Explanations) is a tool used to understand how your model predicts in a certain way. In my last blog, I tried to explain the importance of interpreting our models. Now we will see one of the explainable AI libraries on the act and implement it on a dataset together. If you don’t know anything about explainable AI, go ahead and read my “Explain How Your Model Works Using Explainable AI”.

As data scientists, we should be able to “debug” our model so that it is understandable. Both stakeholders and customers will have questions and…


The Infinite Monkey Theorem is an urban winery operated out of the back alleys of Denver & Austin.

HAHA, Gotcha! Just kidding. Today we will talk about a famous mathematical problem, “The Infinite Monkey Theorem”. The place does exist though, I came across to their website when I was doing a research for this post.

The idea goes back to early 1900’s, it basically says if we have infinite time and infinite resources, we can create even very complex things.

Suppose you have infinite number of monkeys, infinite number of typewriters, paper, ink and time. Monkeys hit the keys randomly…

Can you explain how your model works?

Artificial intelligence techniques are used to solve real-world problems. We get the data, perform some operations to make it clean & ready for the following processes.

We basically pick things from this world and take them into the world of machines, represent it with numbers, and then feed it to a bunch of models. Try to improve them and eventually “the winner model” gets the test data. A vital question comes to the minds :

“ How do we take this result back to real world ? “

Explainable AI (with a cooler name: XAI)

A formal definition: According to…

In this post, we will introduce ourselves to PyTorch. It is developed by Facebook AI research lab in 2016. The library is mostly used for computer vision, machine learning, and deep learning applications. Currently, I am participating in a bootcamp called “Deep Learning with PyTorch: Zero to GANs”. In the scope of this program, we will learn and implement many things by using this library. As I progress through the bootcamp, I aim to share what I have learned here!

If you have no idea about PyTorch, don’t worry. I am also a complete beginner in deep learning. We will…

Learn more about Git and get started!.


“Git” is the best friend and the life savior of developers. As data science enthusiasts, should it be our best friend too? Definitely yes! In this post, I’ll explain what the hell is Git, why we should use it, and how to get started. In the following posts, we will learn how to get comfortable with managing our projects effectively with this technology

Recently I started a challenge where I committed myself to learn something new about Data Science every day (#66daysofdata challenge, you can check out this post for more information:

Buckle up, we are going on an adventure! Yes, you heard it right, I’m starting something brand new and I’m gonna take you with me. Probably most of you have heard the famous #66daysofdata challenge by Ken Jee. I’ve been following the hashtag for sometime and I recently joined the discord server as well. (Such a great and welcoming community!) After some planning and resource hunting, finally I’m starting!!

In case you haven’t heard the challenge, let me summarize the gist. Dou you know something called the 1% Principle? Well, according to this rule,even the slightest improvements matter a lot…

Semanur Kapusızoğlu

Hi! I‘m an industrial engineer passionate about data science and machine learning. I’m here because best way to learn something is to teach it.Hope you enjoy!

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