deep learning

1. Introduction the last decade, all the AI industry has all the time believed in a single unsaid convention: that intelligence can only emerge at scale. We convinced ourselves that for the models to...

is the brand new “hot” role within the tech scene, and lots of individuals are eager to land this job. I see so many posts online saying how you'll be able to turn out...

1. Introduction have a model. You've got a single GPU. Training takes 72 hours. You requisition a second machine with 4 more GPUs — and now you would like your code to truly use...

an Edinburgh-trained PhD in Information Retrieval from Victor Lavrenko’s Multimedia Information Retrieval Lab at Edinburgh, where I trained within the late 2000s, I even have long viewed retrieval through the framework of traditional...

will not be a knowledge quality problem. It will not be a training problem. It will not be an issue you may solve with more RLHF, higher filtering, or a bigger context window. It's a...

Why This Piece Exists of the Fourier Transform — more like an intuition piece based on what I’ve learned from it and its application in sound frequency evaluation. The aim here is to construct...

of a series about distributed AI across multiple GPUs: Introduction Within the previous post, we saw how Distributed Data Parallelism (DDP) hastens training by splitting batches across GPUs. DDP solves the throughput problem, however it...

to be the state-of-the-art object detection algorithm, looked to turn into obsolete due to the looks of other methods like SSD (Single Shot Multibox Detector), DSSD (Deconvolutional Single Shot Detector), and RetinaNet. Finally,...