The Five Faces of Algorithms

The Five Faces of Al...

Or, How We Conceptualize The Invisible Algorithms are everywhere. Sometimes we see traces. Once in a while, we feel the effects. Mostly, we go about our days vaguely aware of an invisible algorithmic presence. The goal: to make invisible algorithms more visible. So, I designed challenges to provide a glimpse into everyday moments and ongoing […]

Classification and Clustering Algorithms

Classification and C...

A famous dialogue you could listen from the data science people. It could be true if we add it’s so challenging at the end of the dialogue. The foremost challenge starts from  categorising the problem itself. The first level of categorising could be whether supervised or unsupervised learning. The next level is what kind of algorithms to get […]

A Stochastic Gradient Descent Implementation in Clojure

A Stochastic Gradien...

Description of the problem Gradient Descent is an algorithm that finds local extremum points of a real valued function with several variables.  As such it is a go-to algorithm for many optimization problems that appear in the context of machine learning.  I wrote an implementation optimizing Linear Regression and Logistic Regression cost functions in Common Lisp in the past. These cost functions […]

A Guide to Gradient Boosted Trees with XGBoost in Python

A Guide to Gradient ...

XGBoost has become incredibly popular on Kaggle in the last year for any problems dealing with structured data. I was already familiar with sklearn’s version of gradient boosting and have used it before, but I hadn’t really considered trying XGBoost instead until I became more familiar with it. I was perfectly happy with sklearn’s version and didn’t think much of […]

Generalizing Abstract Arrays: opportunities and challenges

Generalizing Abstrac...

Introduction: generic algorithms with AbstractArrays Somewhat unusually, this blog post is future-looking: it mostly focuses on things that don’t yet exist. Its purpose is to lay out the background for community discussion about possible changes to the core API for AbstractArrays, and serves as background reading and reference material for a more focused “julep” (a […]

How to visualize decision trees in Python

How to visualize dec...

Decision tree classifier is the most popularly used supervised learning algorithm. Unlike other classification algorithms, decision tree classifier in not a black box in the modeling phase.  What that’s means, we can visualize the trained decision tree to understand how the decision tree gonna work for the give input features. So in this article, you […]

How the Multinomial Logistic Regression Model Works

How the Multinomial ...

In the pool of supervised classification algorithms, the logistic regression model is the first most algorithm to play with. This classification algorithm again categorized into different categories. These categories purely based on the number of target classes. If the logistic regression model used for addressing the binary classification kind of problems it’s known as the […]

Algorithms are Black Boxes, That is Why We Need Explainable AI

Algorithms are Black...

Artificial Intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the beginning of the AI revolution that is upon us, we can already see that artificial intelligence will have a […]

Why Humanizing Algorithms is a Good Idea

Why Humanizing Algor...

Algorithms are taking over the world. Not yet completely and not yet definitely, but they are well on their way to automate a lot of tasks and jobs. This algorithmization offers many benefits for organizations and consumers; boring tasks can be outsourced to an algorithm that is exceptionally well at a very dull task, much […]