machine learning

In my previous article I explained how YOLOv1 works and tips on how to construct the architecture from scratch with PyTorch. In today’s article, I'm going to deal with the loss function used to...

Despite tabular data being the bread and butter of industry data science, data shifts are sometimes missed when analyzing model performance. We’ve all been there: You develop a machine learning model, achieve great results in...

a , a deep learning model is executed on a dedicated GPU accelerator using input data batches it receives from a CPU host. Ideally, the GPU — the dearer resource — needs to...

that frustrating hovering drone from ? The one which learned to descend toward the platform, go through it, after which just… hang around below it eternally? Yeah, me too. I spent a whole afternoon...

! In case you’ve been following along, we’ve come a good distance. In Part 1, we did the “dirty work” of cleansing and prepping. In Part 2, we zoomed out to a high-altitude view of...

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...

the sorts of answers we expect today from Retrieval-Augmented Generation (RAG) systems. Over the past few years, RAG has develop into one in all the central architectural constructing blocks for knowledge-based language models: As...

, 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...