Module 5 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
This is the bridge into Track 2. Neurons, layers, weights, activation functions. The forward pass makes a guess, the loss measures the miss, and backprop and gradient descent nudge the weights toward less wrong. You want all of that as intuition, not calculus. You also want a feel for what “deep” actually buys you, what the GPUs are doing, and what it costs to train one of these.
The idea that matters most here is embeddings. You turn a word, or a company, or a prospect, into a list of numbers that captures its meaning, and similar things end up sitting near each other. Almost everything in RAG and semantic search comes back to this.
Why it matters
Embeddings come up in every conversation about RAG and personalization. If you can explain “similar prospects sit close together in space” without losing the room, the rest of Track 2 lands easily. Skip this and modules 8 through 10 will feel like magic instead of engineering.
Build this
One paragraph that explains embeddings with a sales analogy, clean enough to use with a client and reuse in the RAG module. If you have time, watch the first couple of Karpathy’s Zero to Hero videos and note what clicked.
How to work through it
- 3Blue1Brown’s neural networks series, watched twice
- Jay Alammar’s Illustrated Word2Vec
- Optional: the first two Karpathy Zero to Hero videos
- Write the embeddings-as-sales-analogy paragraph