Recent Posts

May 6, 2026

[MI 4] The BERT Family: A family of different experts

April 7, 2026

Exploring “Linear” in Linear Regression

April 7, 2026

The curious case of R-Squared: Keep Guessing

March 11, 2026

[C1] What Machines Actually Do (And What They Don’t)

March 11, 2026

[ML x] Machine Decision: From One Tree to a Forest

November 2, 2024

[ML 1] AI Paradigm Shift: From Rules to Patterns

November 5, 2025

[ML 1.a] ML Foundations – Linear Combinations to Logistic Regression

November 14, 2025

[ML 1.b] Teaching AI Models: Gradient Descent

November 19, 2025

[ML 2] Making Sense Of Embeddings

November 22, 2025

[ML 2.a] Word2Vec: Start of Dense Embeddings

November 28, 2025

[ML 2.b] Measuring Meaning: Cosine Similarity

December 3, 2025

[ML 2.c] Needle in the Haystack: Embedding Training and Context Rot

February 16, 2026

[MI 3] Seq2Seq Models: Basics behind LLMs

February 13, 2026

[MU 1] Advertising in the Age of AI

December 9, 2025

[EN 1.a] Breaking the “Unbreakable” Encryption – 1

December 13, 2025

[EN 1.b] Breaking the “Unbreakable” Encryption – 2

December 18, 2025

[PET 1] Privacy Enhancing Technologies – Introduction

December 21, 2025

[PET 1.a] Privacy Enhancing Technologies (PETs) — Part 1

December 25, 2025

[PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2

December 30, 2025

[PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3

February 2, 2026

[MI 1] An Intuitive Guide to CNNs and RNNs

November 9, 2025

[MI 2] How CNNs Actually Work

January 16, 2026

How Smart Vector Search Works

Encryption

[EN 1.a] Breaking the “Unbreakable” Encryption – 1

If you’ve spent any time in tech, you’ve heard of AES, RSA, and Diffie-Hellman. We treat them like digital…

Machine Learning Basics

[ML 2.c] Needle in the Haystack: Embedding Training and Context Rot

You’ve probably experienced this: you paste a 50-page document into ChatGPT or Claude, ask a specific question…

Encryption

[EN 1.b] Breaking the “Unbreakable” Encryption – 2

In Part 1, we covered the “Safe” (Symmetric) and the “Mailbox” (Asymmetric). The TL;DR: we use…

Privacy Tech

[PET 1] Privacy Enhancing Technologies – Introduction

Every time you browse a website, click an ad, make a purchase, or train an ML model, data flows through systems.…

Machine Learning Basics

[ML 1.b] Teaching AI Models: Gradient Descent

In the last post, we established the big idea: machine learning is about finding patterns from data instead of writing…

Machine Learning Basics

[ML 2] Making Sense Of Embeddings

When you search on Amazon for “running shoes,” the system doesn’t just look for those exact words…

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embedding

1 Article

[MI 4] The BERT Family: A family of different experts

By Archit Sharma

12 Min Read

You’ve been there. You type “my package never showed up” into a company’s support chatbot, and it confidently responds with a step-by-step guide on how to update your shipping address. Same general topic — shipping. Completely…