⚙️ Optimization in Machine Learning: From Gradient Descent to Newton’s Method
Understand the core optimization techniques used in training machine learning models, including gradient descent, Newton’s method, learning rates, and loss surfaces.
Hoda Osama
🧱 What You’ll Learn Here
📊 Foundations
Clear explanations of the statistics, linear algebra, and calculus you actually need — no more, no less.
🤖 ML Concepts
Build up from intuitive models to CNNs and neural networks — explained visually and step-by-step.
🧠 Problem Solving
Practice with real problems, detailed solutions, and structured problem-solving roadmaps.
🔺 What is the Hessian? Understanding Curvature and Optimization in Machine Learning
Learn how the Hessian matrix extends partial derivatives to second-order curvature, and why it's essential for optimization, convexity, and machine learning algorithms like Newton’s Method.
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.
🔗 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...
📐 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 le...
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, regardles...
From Sample to Population: Basics of Sampling in Statistics
Learn the difference between population and sample, key sampling techniques, and how sampling impacts data science and machine learning accuracy.
Understanding Binomial Distribution
Learn how the binomial distribution works in statistics and machine learning. Explore real-world examples, formulas, and how to calculate success probabilities in binary events.
Understanding Z-Distribution and Using the Z-Table
Master the Z-distribution — a key concept in data science that transforms values into z-scores, enabling outlier detection, standardization, and easier comparison across datasets. Essential for sta...