Doruk's Sporadic Musings

a collection of posts about Machine Learning and other stuff

Hierarchical Clustering and its Applications

26 October 2018

Clustering is one of the most well known techniques in Data Science. From customer segmentation to outlier detection, it has a broad range of uses, and different techniques that fit different use cases. In this blog post we will take a look at hierarchical clustering, which is the hierarchical application of clustering techniques.

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Recommender Systems: From Filter Bubble to Serendipity

09 October 2018

Recommender systems power a lot of our day to day interactions with the content we see on the internet. With over 2.5 quintillion bytes of data created each day, the last two years alone make up 90% of the data in the world [1]. We produce content at a level that is simply impossible to consume in one lifetime, and that makes recommender systems inevitable. However, as Uncle Ben said, with great power comes great responsibility. Here I talk about some of the practical and ethical problems that recommender systems raise, and how we can go about solving them.

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Representation Learning through Matrix Factorization

10 September 2018

Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) has been around for a while, and have been successfully utilized for learning intermediary representations of data for quite some time. This post will be recap on what they actually do, and how they work.

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Why you should use PCA before Decision Trees

11 August 2018

Dimensionality Reduction techniques have been consistently useful in Data Science and Machine Learning. It can reduce training times, allow you to remove features that do not hold any predictive value, and it even works for noise reduction. In this blog post, we are going to focus on why it might even make your classifier perform better.

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Introducing Books2Rec

14 May 2018

I am proud to announce Books2Rec, the book recommendation system I have been working for the last couple of months, is live. Using your Goodreads profile, Books2Rec uses Machine Learning methods to provide you with highly personalized book recommendations. Don’t have a Goodreads profile? We’ve got you covered - just search for your favorite book.

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Representation Learning: An Introduction

24 February 2018

Representation Learning is a relatively new term that encompasses many different methods of extracting some form of useful representation of the data, based on the data itself. Does that sound too abstract? That’s because it is, and it is purposefully so. Feature learning, Dimensionality reduction, Data reduction, and even Matrix Factorization are all parts of Representation Learning.

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