Understanding the Jacobian – A Beginner’s Guide with 2D & 3D Examples
Understand the Jacobian matrix and vector through step-by-step examples, visuals, Python code, and how it powers optimization and machine learning.
Hoda Osama
🔗 Chain Rule, Implicit Differentiation, and Partial Derivatives (Calculus for ML)
Understand the Chain Rule, Implicit Differentiation, and Partial Derivatives with simple examples. Learn how to differentiate composite and multivariable functions step by step — a must for machine learning.
📐 Understanding Gradients and Partial Derivatives (Multivariable Calculus for Machine Learning)
Learn what gradients and partial derivatives are, how to compute them step-by-step, and how they relate to slope in multivariable functions. With examples and Python code.
🔍 What is a Derivative? (Beginner’s Guide to Calculus for ML)
Learn what a derivative is, how it relates to slope and gradient, and why it's essential in calculus and machine learning — explained with examples and visuals.
🔁 From Limits to Smoothness: Transformations, Limits, Continuity & Differentiability
Understand functions as geometric transformations, visualize limits, and grasp continuity and differentiability with Python and math-based insights.
🌄 Visualizing Multivariable Functions: Contour Plots, Vector-Valued Functions & Vector Fields (Beginner's Guide)
Learn how to visualize and understand contour plots, vector-valued functions, and vector fields with Python code and clear examples. A beginner-friendly guide tying math concepts to machine learning.
🧮 Understanding Functions: The Foundation of Calculus (and Machine Learning!)
Learn the fundamentals of functions in calculus, including scalar and vector-valued functions, domain, range, examples, and best practices. Perfect for beginners in math, statistics, and machine learning.
Sampling Distribution of the Sample Proportion
🎯 The Sampling Distribution of the Sample Proportion In a population, the proportion is the number of successful outcomes over the total number of cases. This proportion is denoted by \( \beta \...
Population, Sample, and Sampling Distributions Explained
Understanding the difference between population, sample, and sampling distributions is essential for data analysis, statistics, and machine learning. In this guide, we’ll explain each type of distr...
Understanding the Sampling Distribution of the Sample Mean and the Central Limit Theorem
The Central Limit Theorem (CLT) is a cornerstone of statistics and data science. It states that the distribution of sample means approaches a normal distribution as the sample size grows, regardless of the original population’s distribution. This powerful concept is essential for making statistical inferences, building confidence intervals, and running hypothesis tests. Whether you're analyzing user behavior in A/B testing or validating machine learning metrics, understanding CLT helps you interpret data more reliably and make better predictions.