Classical ML and Deep Learning
Formulating and Implementing XGBoost From Scratch
An extensive visual guide to never forget how XGBoost works.
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Classical ML and Deep Learning
Formulating the Principal Component Analysis (PCA) Algorithm From Scratch
Approaching PCA as an optimization problem.
Classical ML and Deep Learning
You Are Probably Building Inconsistent Classification Models Without Even Realizing
The limitations of always using cross-entropy loss in ordinal datasets.
Classical ML and Deep Learning
Why R-squared is a Flawed Regression Metric?
The lesser-known limitations of the R-squared metric.
Classical ML and Deep Learning
Generalized Linear Models (GLMs): The Supercharged Linear Regression
The limitations of linear regression and how GLMs solve them.
Classical ML and Deep Learning
Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?
What are we missing here?
Classical ML and Deep Learning
Why Do We Use log-loss To Train Logistic Regression?
The origin of log-loss.
Classical ML and Deep Learning
Why Do We Use Sigmoid in Logistic Regression?
The origin of the Sigmoid function and a guide on modeling classification datasets.
