https://www.tilestats.com 1. The sample variance (1:00) 2. Sample variance vs population variance (02:40) 3. Unbiased vs biased estimator (03:45) 4. The mathematical proof (05:30) 5. The expectation rules (12:30)
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Deep learning - explained simply | Early Stopping & Dropout
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Convolutional Neural Network (CNN) – explained simply
Autoencoders - simply explained
PCA vs PCoA (Multidimensional scaling) - explained
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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