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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
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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
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…

Model Intuition

[MI 2] How CNNs Actually Work

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

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

How Smart Vector Search Works

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

Privacy Tech

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

How Your Data Gets Protected Every time you browse a website, click an ad, or make a purchase, data flows through…

Concepts

The curious case of R-Squared: Keep Guessing

Most explanations of R-squared start with a formula: Then they say something like “the proportion of variance…

Browse Tag

MSE

1 Article

The curious case of R-Squared: Keep Guessing

Archit Sharma By Archit Sharma
5 Min Read

Most explanations of R-squared start with a formula: Then they say something like “the proportion of variance explained by the model” and move on. And you nod, and you write it down, and somewhere in the back of your head a small voice says:…

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Concepts

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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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