data science

a decade working in analytics, I firmly imagine that observability and evaluation are essential for any LLM application running in production. Monitoring and metrics aren’t just nice-to-haves. They ensure your product is functioning...

Imagine that we have now three measures: Sales Amount Sales Amount Budget Sales Amount PY Once we take a look at a table with these numbers, we see this (assuming that we have now current data until the...

, I’ve at all times had a knack for data storytelling. You already know, finding the patterns and constructing visuals that really made sense. I’d learned the principles, and truthfully, I believed I had all...

use gradient descent to seek out the optimal values of their weights. Linear regression, logistic regression, neural networks, and enormous language models all depend on this principle. Within the previous articles, we used...

within the , a series of each day programming challenges released throughout December, for the primary time. The each day challenges normally contain two puzzles constructing on an identical problem. Although these challenges...

TLDR in healthcare often output binary decisions comparable to disease or no disease, which by themselves cannot produce a meaningful AUC. AUC remains to be the usual technique to compare risk and detection models in...

, we'll implement AUC in Excel. AUC is normally used for classification tasks as a performance metric. But we start with a confusion matrix, because that's where everyone begins in practice. Then we'll see why a...

concentrate on isolated tasks or easy prompt engineering. This approach allowed us to construct interesting applications from a single prompt, but we're beginning to hit a limit. Easy prompting falls short after we...