Recent studies have shown that vision transformer (ViT) models can attain better results than most state-of-the-art convolutional neural networks (CNNs) across various image recognition tasks, and can do so while using considerably fewer computational resources. This has led some researchers to propose ViTs could replace CNNs in this field.However, despite their promising performance, ViTs areContinue Reading
Written by Dheepan Ramanan (@dheepan_ramanan), Data Scientist and Ivan Kopas (@ivan_kopas), Machine Learning Engineer Last Friday ARK Invest released a new price target for Tesla as well as an updated, open-source model. The scale of autonomous ride hailing networks and ARK’s estimate for Tesla’s dominance emerged as the most contentious elements in the model. These components contribute nearly 50% of ARK’s $3k 2025 price target. On twitter there has been considerable debate on the size of the Robotaxi market and Tesla’s lead in autonomous driving, questioning whether Tesla’s Full Self Driving (FSD) approach can be reverse-engineered and replicated by the competitors.
For instance, you might learn in an online course how to run a YOLO network, but a real-world use case might asks for 7 YOLO networks in distributed GPUs and a HydraNet architecture. What the heck is…
This year was huge for me in the field of machine learning and computer vision in particular. A bit more than a year ago I would never believe that I would spend a week abroad not…
The purpose of deep learning is to learn a representation of high dimensional and noisy data using a sequence of differentiable functions, i.e., geometric transformations, that can perhaps be used…