Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.
Learn how distributed training works in pytorch: data parallel, distributed data parallel and automatic mixed precision. Train your deep learning models with massive speedups.
Learn how to implement the infamous contrastive self-supervised learning method called SimCLR. Step by step implementation in PyTorch and PyTorch-lightning
A review of state of the art vision-language models such as CLIP, DALLE, ALIGN and SimVL
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 create meaningful optimization problems.
Explore what is neural architecture search, compare the most popular,SOTA methodologies and implement it with nni
A list of the top books to learn deep learning divided into four distinct categories. Personal reviews are included for each one of them.
Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset
Discorver how to formulate and train Spiking Neural Networks (SNNs) using the LIF model, and how to encode data so that it can be processed by SNNs