This rubric focuses on computer vision, and how computers can gain high-level understanding from data derived from digital images or videos. Topics also include haptics and other examples of computer sensory input, as well as concepts such as generative adversarial networks and AI-driven image recognition.
There is no statistical test that assesses whether a sequence of observations, time series, or residuals in a regression model, exhibits independence or not. Typically,… Read More »New Tests of Randomness and Independence for Sequences of Observations
Interesting picture summarizing several types of techniques used in machine learning, contrasting unsupervised learning with unsupervised learning and reinforcement learning. The difference between supervised and… Read More »Machine Learning with Applications in One Picture
I can’t find anymore where this chart, featuring relations between distributions, was first published. I remember seeing it on the Cloudera blog. Another shorter one… Read More »Statistical Distributions in One Picture
In my previous article, we analyzed the COVID-19 data of Turkey and selected the cubic model for predicting the spread of disease. In this article,… Read More »Model Selection: Adjusted Coefficient of Determination-Variance Tradeoff
This is the 2nd part of the article on a few applications of Fourier Series in solving differential equations. All the problems are taken from… Read More »Fourier Series and Differential Equations with some applications in R and Python (Part 2)
The Problem Millions of people are forced to leave their current area of residence or community due to resource shortage and natural disasters such as… Read More »Using Neural Networks to Predict Cimate Change, Droughts, and Conflict Displacements
Generative Adversarial Networks (GANs) software is software for producing forgeries and imitations of data (aka synthetic data, fake data). Human beings have been making fakes,… Read More »Generative Adversarial Networks (GANs) & Bayesian Networks