Traditionally, datasets in Deep Learning applications such as computer vision and NLP are typically represented in the euclidean space. Recently though there is an...
Self-supervised learning, which is widely popular in computer vision, focuses on extracting representations from unsupervised visual data. This article delves into the SWAV method,...
High energy consumption and the increasing computational cost of Artificial Neural Network (ANN) training tend to be prohibitive. Furthermore, their difficulty and inability...
Transformers are a significant trend in computer vision, and I recently provided an overview of some remarkable advancements. In this instance, I will use...
Neural Architecture Search (NAS) automates the design of neural networks’ topology to achieve the best performance on a specific task. The goal is to...
In today’s digital landscape, companies have to work harder than ever to capture their audience’s attention. A strong online presence is crucial, so how...
This article demystifies the ML learning modeling process under the prism of statistics.
We will understand how our assumptions on the data enable us to...
