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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
Machine Learning Basics

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

Every piece of software you’ve ever shipped works the same way. A developer thinks through the logic and writes…

Machine Learning Basics

How Smart Vector Search Works

In the ever-evolving world, the art of forging genuine connections remains timeless. Whether it’s with colleagues,…

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…

Model Intuition

[MI 3] Seq2Seq Models: Basics behind LLMs

When you use Google Translate to turn a complex English sentence into Spanish, or when you ask Gemini to summarize a…

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…

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…

Browse Tag

Unsupervised Learning

1 Article

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

Archit Sharma By Archit Sharma
17 Min Read

Every piece of software you’ve ever shipped works the same way. A developer thinks through the logic and writes explicit rules — if the user clicks here, do this; if the input exceeds 100, reject it; if the date passes the deadline, send an email.…

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Machine Learning Basics

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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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