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icon icon Building AI Intuition

Connecting the dots...

icon icon Building AI Intuition

Connecting the dots...

  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
Close

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Recent Posts
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
Model Intuition

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

Every time a bank approves or denies a loan in milliseconds, every time Netflix decides what to recommend next, every…

Machine Learning Basics

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

Every machine learning model — from simple house price predictors to neural networks with billions of parameters —…

Privacy Tech

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

Privacy-Preserving Computation and Measurement In Part 1, we covered how organizations protect data internally —…

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…

Privacy Tech

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

Secure Collaboration Without Sharing Raw Data In Part 1, we covered how individual organizations protect data…

Concepts

Exploring “Linear” in Linear Regression

Linear regression is one of those things you learn early, use forever, and never quite slow down to inspect. So…

Browse Tag

grovers-algorithm

1 Article

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

Archit Sharma By Archit Sharma
7 Min Read

In Part 1, we covered the “Safe” (Symmetric) and the “Mailbox” (Asymmetric). The TL;DR: we use high-speed symmetric safes to store our data, but we rely on mathematical “couriers” to deliver the keys. Today,…

Read More
Encryption

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Model Intuition

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Recent Posts

  • Exploring “Linear” in Linear Regression
  • The curious case of R-Squared: Keep Guessing
  • [C1] What Machines Actually Do (And What They Don’t)
  • [ML x] Machine Decision: From One Tree to a Forest
  • [MI 3] Seq2Seq Models: Basics behind LLMs
  • [MU 1] Advertising in the Age of AI
  • [MI 1] An Intuitive Guide to CNNs and RNNs
  • How Smart Vector Search Works
  • [PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
  • [PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2
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