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

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Recent Posts
March 1, 2026
Teaching AI Models: Gradient Descent
March 1, 2026
Needle in the Haystack: Embedding Training and Context Rot
March 1, 2026
Measuring Meaning: Cosine Similarity
February 28, 2026
AI Paradigm Shift: From Rules to Patterns
February 16, 2026
Seq2Seq Models: Basics behind LLMs
February 16, 2026
Word2Vec: Start of Dense Embeddings
February 13, 2026
Advertising in the Age of AI
February 8, 2026
Breaking the “Unbreakable” Encryption – Part 2
February 8, 2026
Breaking the “Unbreakable” Encryption – Part 1
February 8, 2026
ML Foundations – Linear Combinations to Logistic Regression
February 2, 2026
Privacy Enhancing Technologies – Introduction
February 2, 2026
Privacy Enhancing Technologies (PETs) — Part 3
February 2, 2026
Privacy Enhancing Technologies (PETs) — Part 2
February 2, 2026
Privacy Enhancing Technologies (PETs) — Part 1
February 2, 2026
An Intuitive Guide to CNNs and RNNs
February 2, 2026
Making Sense Of Embeddings
November 9, 2025
How CNNs Actually Work
August 17, 2025
How Smart Vector Search Works
Machine Learning Basics

Measuring Meaning: Cosine Similarity

Post 2b/N In the previous posts, we established that embeddings turn everything into points in space and that Word2Vec…

Machine Learning Basics

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…

Model Intuition

How CNNs Actually Work

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

Machine Learning Basics

ML Foundations – Linear Combinations to Logistic Regression

Post 1a/N Every machine learning model — from simple house price predictors to neural networks with billions of…

Privacy Tech

Privacy Enhancing Technologies (PETs) — Part 3

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

Machine Learning Basics Model Intuition

Needle in the Haystack: Embedding Training and Context Rot

Post 2c/N You’ve probably experienced this: you paste a 50-page document into ChatGPT or Claude, ask a specific…

Browse Tag

linear-combination

1 Article

ML Foundations – Linear Combinations to Logistic Regression

Archit Sharma By Archit Sharma
11 Min Read

Post 1a/N Every machine learning model — from simple house price predictors to neural networks with billions of parameters — starts with the same fundamental building block: the linear combination. Take some inputs, multiply each by a weight, and add…

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Recent Posts

  • Teaching AI Models: Gradient Descent
  • Needle in the Haystack: Embedding Training and Context Rot
  • Measuring Meaning: Cosine Similarity
  • AI Paradigm Shift: From Rules to Patterns
  • Seq2Seq Models: Basics behind LLMs
  • Word2Vec: Start of Dense Embeddings
  • Advertising in the Age of AI
  • Breaking the “Unbreakable” Encryption – Part 2
  • Breaking the “Unbreakable” Encryption – Part 1
  • ML Foundations – Linear Combinations to Logistic Regression
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