Introduction to Gradient Decent
The gradient decent approach is used in many algorithms to minimize loss functions. In this introduction we will see how exactly a gradient descent works.… Read More »Introduction to Gradient Decent
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FrankRaulf
How to make time-data cyclical for prediction?
There are many ways to deal with time-data. Sometimes one can use it as time-series to take possible trends into account. Sometimes this is not… Read More »How to make time-data cyclical for prediction?
Setting the Cutoff Criterion for Probabilistic Models
For decision making, human perception tends to arrange probabilities into above 50% and below – which is plausible. For most probabilistic models in contrast, this… Read More »Setting the Cutoff Criterion for Probabilistic Models
Naive Bayes Classifier using Kernel Density Estimation (with example)
Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities… Read More »Naive Bayes Classifier using Kernel Density Estimation (with example)
Which one is faster in multiprocessing, R or Python?
This post is the third one of a series regarding loops in R an Python. The first one was Different kinds of loops in R.… Read More »Which one is faster in multiprocessing, R or Python?
Different kinds of loops in R.
Normally, it is better to avoid loops in R. But for highly individual tasks a vectorization is not always possible. Hence, a loop is needed… Read More »Different kinds of loops in R.
Loop-Runtime Comparison R, RCPP, Python
The positive reactions on my last post: “Different kinds of loops in R” lead me to compare some different versions of loops in R, RCPP… Read More »Loop-Runtime Comparison R, RCPP, Python
Data Quality Maintenance
How to implement supporting data-quality-maintenance-systems using AI? How to find out which method performs best? Is it possible to further improve the results? How to… Read More »Data Quality Maintenance