https://www.tilestats.com/ 1. Deep learning 2. A simple example of an ANN (01:30) 3. Add a hidden layer (06:00) 4. DNN and overfitting (07:50) 5. An example with continuous data (09:40) 6. Early stopping (14:40) 7. Dropout (17:30)
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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