A science blog about my spare time data analysis projects.
Efficient data processing lies at the heart of modern machine learning. Kernel smoothing, a versatile non-parametric technique, benefits significantly from performance improvements through optimized algorithms and frameworks. In this post, …
Predicting stock prices is akin to the attempts of alchemists in the Middle Ages to transmute lead into gold. Just as alchemists sought to unlock the secrets of transformation, statisticians …
In many applications actuaries, data scientists or researchers are confronted with datasets like shown in Table 1. While nominal variables, as in the first or second column, can be used …
Introduction A while ago I applied NLP strategies to implement an algorithm that is capable to guess a password. Since then a new method called transformer was developed and successfully …
In this article we will show a way to do high performance parallel computing at a Kubernetes cluster using task. A primary focus is that we want to archive the …
1. Setup In a previous post it was shown how to speed up the computation of a kernel density using the Fast Fourier Transform. Conceptually a kernel density is not …
1. Setup This Post is about how to speed up the computation kernel density estimators using the FFT (Fast Fourier Transform). Let be be a random sample drawn from an …
In a previous post I covered how to apply classical linear estimators like support vector machines or logistic regression to a non-linear dataset using the kernel method. This article can …
1. Introduction This blog post is about Support Vector Machines (SVM), but not only about SVMs. SVMs belong to the class of classification algorithms and are used to separate one …
1. Kernel Regression using Pyspark In a previous article I presented an implementation of a kernel denisty estimation using pyspark. It is thus not difficult to modify the algorithm to …
1. A Nonparametric Density implementation in Spark One of my previous blog post concerns about nonparametric density estimation. In this post i presented some Matlab code. An advantage of this …
1. Functional Regression Let the covariate be an at least twice continuously differentiable random function defined wlog. on an interval and the corresponding the response. For simplicity we assume centered …
1.) Functional Principal Component Analysis Let be a centered smooth random function in , with finite second moment . Without loss of generality we assume instead of some arbitrary compact …
1. Perform the Analyze To start with, we load the “rockyou.txt.tar.gz” password list using wget. I’m not sure if it is legal to provide a link for the list, therefore …
1. Test the Enviroment 1.1 Simulation of a Brownian Motion The purpose of the first notebook entry is to check if matplotlib is correctly installed. We simulate 20 Brownian Motions …