Stay Inquisitive!

I’m a firm believer in the Feynman technique, the idea that teaching is the best way to learn. This blog is my space to apply that method by breaking down complex ideas and explaining them in simple, intuitive terms. It helps me reinforce my own understanding, and I hope it helps you too. Thanks for stopping by, and happy reading!

Probability Models and Axioms

Probability
Mathematics

Building the foundation of probability theory from scratch: sample spaces, events, and the three axioms that govern all of probability.

Mar 25, 2026
38 min

Linear Regression

Machine Learning
Supervised Learning
Mathematics

A first-principles introduction to linear regression, from eyeballing a line on a scatter plot to deriving the closed-form solution and its probabilistic justification.

Mar 22, 2026
38 min

Linear Algebra for Machine Learning: A First-Principles Guide

Linear Algebra
Mathematics

A comprehensive, story-driven guide to linear algebra, building from concrete examples to the mathematical tools that power machine learning. Covers matrix operations, eigenvalues, matrix calculus, and least squares.

Mar 10, 2026
82 min

Introduction to Machine Learning

Machine Learning

A rigorous introduction to machine learning, covering its definition, core paradigms, and the role of data, with mathematical notation and intuitive examples.

Dec 24, 2025
20 min

Introduction to APIs

FastAPI

A practical introduction to APIs covering their types, protocols (REST, SOAP, GraphQL, gRPC, WebSocket), lifecycle, and authentication best practices.

Jul 15, 2025
17 min

Network of Sigmoid Neurons

Deep Learning

An exploration of multilayer networks of sigmoid neurons and their ability to approximate any continuous function, illustrating the universal approximation theorem with tower constructions in 1D and 2D.

Jul 3, 2025
8 min

Notational Reference for my Posts

Reference

This post serves as a notational reference in which you can find all the notational conventions I use in my blog posts.

Jul 1, 2025
3 min

Sigmoid Neuron

Deep Learning

An introduction to the sigmoid neuron and logistic activation for real-valued modeling, highlighting probabilistic outputs, differentiability, and gradient descent. Includes a Taylor series intuition, a toy example, and pseudocode for parameter updates.

Jun 26, 2025
23 min

Hadoop Distributed File System

Data Engineering

An in-depth guide to the Hadoop Distributed File System (HDFS), covering its architecture, blocks, replication, fault tolerance, rack awareness, and how read/write operations are handled for scalable and reliable big data storage.

Jun 25, 2025
31 min

Hadoop

Data Engineering

An overview of Hadoop, its core properties, and its ecosystem of components like HDFS, MapReduce, YARN, Hive, Pig, and more. This post explains how Hadoop enabled distributed storage and processing, and why it remains foundational for modern big data technologies.

Jun 24, 2025
5 min

Network of Perceptrons (MLP)

Deep Learning

An exploration of multilayer perceptrons (MLPs) and their ability to implement any Boolean function, including non-linearly separable ones like XOR. This post illustrates how hidden layers expand representation power and generalizes the idea to networks with more inputs.

Jun 23, 2025
9 min

Perceptron

Deep Learning

A deep dive into the perceptron, a fundamental binary classifier that extends the McCulloch-Pitts neuron with learnable weights and bias. This post explains its working, geometric interpretation, learning algorithm, and limitations on non-linearly separable data like XOR.

Jun 14, 2025
26 min

McCulloch-Pitts Neuron

Deep Learning

An introduction to the McCulloch-Pitts neuron, one of the earliest computational models of the brain. This post builds the model from a simple movie-recommendation scenario, then shows how it implements Boolean functions and reveals its geometric nature as a linearly separable classifier.

Jun 12, 2025
16 min
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