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First encounter with neural networks
Hmm… that really was the first thing I did when I started the Applied Data Science program. But since I’m now stuck and tired of my ugliest ever clustering algorithm, I can’t write about this monstrosity before it starts to somehow work. So, back to the beginnings.. All AI is based on those magical matrices.…
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Second encounter with PCA
In my previous post about PCA, I said that PCA is like a beautiful and playful woman. Oh boy, was I so wrong. Like almost always when it comes to women. I assumed that the reconstruction error for principal component analysis is significant for non-linear data. If it were true, it would be easy to…
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First encounter with Principal Component Analysis
My first feeling when seeing PCA were very mixed: this is some magic, it’s awesome, it’s stupid, it can’t do any good, it’s not readable, it will do great stuff, it won’t help anything… So, I needed to understand it better and run some small experiment. irst assumption about PCA is that it finds linear…
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First encounter with clustering
Unsupervised learning is as challenging to comprehend as solving the Gordian Knot, precisely because it starts with the fundamental unknown—what to expect is not predefined, capturing the very essence of its complexity. However, one aspect particularly struck me. There’s no simple answer to the question, ‘Are there clusters in the data?’ Given the plethora of…