[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 — starts with the same fundamental building block: the linear combination. Take some inputs, multiply each by a weight, and add them up.…
Read More[ML 2] Making Sense Of Embeddings
When you search on Amazon for “running shoes,” the system doesn’t just look for those exact words – it also shows you “jogging sneakers,” “athletic footwear,” and “marathon trainers.” When…
Read More[ML 2.a] Word2Vec: Start of Dense Embeddings
When you type a search query into Google or ask Spotify to find “chill acoustic covers,” the system doesn’t just look for those exact letters. It understands that “chill” is related to “relaxing” and…
Read More[MU 1] Advertising in the Age of AI
When you search for a product today, ads quietly shape what you notice. When you scroll Instagram, ads compete for slices of your attention. For decades, advertising has existed because humans have a simple limitation: we cannot evaluate everything. This…
Read More[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 high-speed symmetric safes to store our data, but we rely on mathematical “couriers” to deliver the keys. Today,…
Read More[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. Companies need this data — for analytics, measurement, personalization, and product improvement. But they also have legal, ethical, and…
Read More[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 dozens of systems. Companies need this data for analytics, measurement, and personalization—but they also have legal and ethical obligations…
Read More[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 internally — minimization, anonymization, query controls, and differential privacy. But modern business often requires multiple parties to…
Read More[PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
Privacy-Preserving Computation and Measurement In Part 1, we covered how organizations protect data internally — minimization, anonymization, query controls, and differential privacy. In Part 2, we explored secure collaboration — clean rooms, identity…
Read More[MI 1] An Intuitive Guide to CNNs and RNNs
When your phone recognizes “Hey Siri,” a CNN is probably listening. When Google Translate converts your sentence into French, an RNN (or its descendants) is doing the heavy lifting. Both are neural networks, but they’re built for fundamentally different…
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