Day 7 on Dense Embedding — Capturing Semantic Meaning with Vector Representations spent the whole class on the dense half of the embedding world. Toda...
Parathan ThiyagalingamDay 6 on Embeddings — Semantic Similarity, Cosine, and Dense vs Sparse closed with the dense-versus-sparse split and a small Ollama demo. Today we slo...
Parathan ThiyagalingamDay 5 wrapped up chunking and PDF processing. Today we go one layer deeper into embeddings themselves, the piece of the pipeline that actually decides...
Parathan ThiyagalingamDay 4 promised PDF processing for today. Before we get there, two more chunking flavours worth meeting first. Sliding chunking and token-based chunkin...
Parathan ThiyagalingamDay 3 is called "semantic chunking", but I only glanced at it. Today, we slow down. The full recipe: working code with sentence-transformers, where th...
Parathan ThiyagalingamDay 2 turned text into embeddings and packed them into a vector database. But there is a step before embedding that quietly decides whether the whole ...
Parathan ThiyagalingamDay 1 ended with a teaser. The model "embeds" text into numbers and stores them in a "vector database". Today, we make that concrete. What an embeddin...
Parathan ThiyagalingamIf you have asked ChatGPT something like, "What were my company's Q3 sales numbers?" or "Summarise the contract I uploaded last week", you have probab...
Parathan Thiyagalingam