As previously mentioned, input data for a neural network must first be transformed into word embeddings.
Notes on systems, LLMs, and the industry building them.
I'm Wenhao, a software engineer. I write up system design problems I've worked through, what I'm learning about LLMs, notes from shipping side projects like ResumeMatch, and the occasional take on layoffs and where the industry is heading.
Given a couple of words, can we generate the next word? Or, given a sentence, can we determine whether each word expresses a positive or negative…
In the previous sections, we saw how matrix operations can be used to calculate neural outputs using linear transformations followed by nonlinear…
The essence of neural networks lies in progressively applying activation functions to linear transformations.
Let's start with a simple linear function: y = wx + b Q: What's a good w and b?
What if I told you that everything we know about the world - every rule, every pattern, every piece of logic - can be described by functions?