deep learning

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,...

wheels sometimes appear like they’re going backward in movies? Or why an inexpensive digital recording sounds harsh and metallic in comparison with the unique sound? Each of those share the identical root cause...

which have pervaded nearly every facet of our day by day lives are autoregressive decoder models. These models apply compute-heavy kernel operations to churn out tokens one after the other in a way...

: Overparameterization, Generalizability, and SAM The dramatic success of recent deep learning — especially within the domains of Computer Vision and Natural Language Processing — is built on “overparameterized” models: models with good enough parameters to memorize the training data...

took the world of autonomous driving by storm with their recent AlpamayoR1 architecture integrating a big Vision-Language Model as a causally-grounded reasoning backbone. This release, accompanied by a brand new large-scale dataset and...

is a component of a series about distributed AI across multiple GPUs: Introduction Before diving into advanced parallelism techniques, we want to know the important thing technologies that enable GPUs to speak with one another. But why...