Embeddings & Representation Learning

This subject assumes Neural Networks & Backpropagation, Training Deep Networks: Optimizers, Schedules & Regularization and The PyTorch Training Loop from Scratch, and builds on Sequence Models: From RNNs to Attention and Convolutional Networks & Vision Basics for the encoders that produce the vectors. It is about how embeddings are learned (objectives, negatives, temperature, towers) and how they are evaluated and operated. Choosing a chunk size and an off-the-shelf embedding model for a RAG pipeline is Chunking and Embedding Strategies, approximate-nearest-neighbour indexes and hybrid search are Vector Databases and Hybrid Search, and the surrounding retrieval-then-ranking architecture is Ranking & Recommendation System Architecture; here we stop at the point where the vectors exist and are being compared.

Embedding interviews follow a pattern: "how would you represent users and items? what loss? where do the negatives come from? how do you know the new model is better? what happens when you retrain?" Every answer reduces to four decisions: what pairs the loss calls similar, what it calls dissimilar, how similarity is scored, and how you measure success. This lesson gives the formulas and numbers behind each, verified by execution, and the traps that produce silently wrong retrieval.