How do AI and neural networks work?

Deep learning - explained simply | Early Stopping & Dropout

How do AI and neural networks work?

Convolutional Neural Network (CNN) – explained simply

Autoencoders - simply explained

PCA vs PCoA (Multidimensional scaling) - explained

SPSS for beginners in 22 minutes - enter data, plot and t-test

Recurrent neural network (RNN) - explained super simple

Stochastic gradient descent (SGD) vs mini-batch GD | iterations vs epochs - Explained

How to compute a p value and extract a critical value in R

Multinomial logistic regression | softmax regression | explained

Why we divide by n-1 when calculating the sample variance – the proof | unbiased estimator

Expected value vs mean

Bootstrap confidence intervals - explained

How to identify and deal with outliers | The 1.5 IQR rule | Boxplots

Bayesian statistics - the basics

How to check normal distribution | The normality assumption

PERMANOVA and permutation tests - explained

Statistical power - Parametric vs Nonparametric test

Meta-analysis | The inverse variance method | Forest plot in R

The Mantel-Haenszel method - clearly explained | deal with confounding

Understanding the odds ratio (OR) and the rare disease assumption | OR = RR?

Relative risk - how to calculate and interpret | 95% CI

Odds vs Probability - explained

The SIR model | the math of epidemics - explained with a simple example