Explaining Vector Embeddings to your Mom

So, Hitesh Choudhary and Piyush Garg sir ne ek assignment diya – "Explain vector embeddings to your mom." And I thought, easy hoga… but phir samjha ki simple language me samjhana is actually tough. So Mom, yeh blog aapke liye.

Starting scene

Mom, aapko yaad hai jab humne “dog” ka photo dekha tha aur turant bola tha – “Yeh dog hai!”? Humne usko identify kiya kyunki humare dimaag me already dog ka ek mental image saved tha. Machine ke liye bhi yeh kaam hota hai – but machine ke paas photo nahi, numbers hote hain.

Aur woh numbers ka jo special representation hota hai, usko bolte hain vector embeddings.

Embeddings ka matlab kya hai?

Simple shabdon me – embedding = words (ya koi bhi data) ka mathematical translation.

For example:

"Dog" ko machine ke liye bana diya ek list of numbers me: [0.12, -0.98, 0.55, ...]

"Cat" ka numbers ka set thoda similar hoga dog se, kyunki dono animals hain.

Ye numbers ka set ek vector hota hai, aur is tarah ka conversion embedding ke through hota hai.

2D ya 3D graph ka example

Imagine ek graph banaya jisme x-axis aur y-axis hai:

"Cat" aur "Dog" ek dusre ke paas plot ho jayenge (kyunki similar meaning hai)

"Eiffel Tower" ka point door hoga, kyunki uska dog se koi lena dena nahi

To embeddings basically har cheez ko ek jagah map kar dete hain jaha similar cheezein paas hoti hain, alag cheezein door hoti hain.

Dimensionality ka masala

Ab sirf 2D me plot karna easy hota hai dekhne me, but real life me yeh vectors ke 100s ya 1000s dimensions hote hain. Jaise OpenAI ke kuch models ka vector size 1536 dimensions hota hai.

Hum dekh nahi sakte, but machine inhi high-dimensional spaces me kaam karti hai.

Position ka khel – Positional Encoding

Mom, imagine aapko ek list of numbers diye, par order change kar diya. Aap confuse ho jaoge na? Machine bhi confuse hoti hai agar sequence ka order na pata ho.

Isliye positional encoding hota hai – jo machine ko batata hai ki kaunsa word kahan aata hai.

Self-Attention – Chugli system

Machine ke andar ek system hota hai jise bolte hain self-attention. Ye har vector ko allow karta hai ki dusre vectors se baat kare:

"Train" vector dekhte hi samajh jata hai "is going" ke saath uska zyada relation hai.

"Dog" vector dekhte hi samajh jata hai "cute" ya "sleeping" ke saath uska zyada relation hai.

Aise machine samajh banati hai ki kaunse words ek dusre se connected hain.

Final Tadka – Why should we care?

Vector embeddings ki wajah se hi aaj aap ChatGPT se baat kar pa rahi ho, Google Translate use kar pa rahi ho, YouTube pe similar videos dekh pa rahi ho.

It’s like machine ka understanding ka foundation – bina embeddings ke AI ka dimaag kaam nahi karega.