Explore our latest articles
BYOL tutorial: self-supervised learning on CIFAR images with code in Pytorch
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.
How distributed training works in Pytorch: distributed data-parallel and mixed-precision training
Learn how distributed training works in pytorch: data parallel, distributed data parallel and automatic mixed precision. Train your deep learning models with massive speedups.
Self-supervised learning tutorial: Implementing SimCLR with pytorch lightning
Learn how to implement the infamous contrastive self-supervised learning method called SimCLR. Step by step implementation in PyTorch and PyTorch-lightning
Vision Language models: towards multi-modal deep learning
A review of state of the art vision-language models such as CLIP, DALLE, ALIGN and SimVL
Understanding Maximum Likelihood Estimation in Supervised Learning
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.
Neural Architecture Search (NAS): basic principles and different approaches
Explore what is neural architecture search, compare the most popular,SOTA methodologies and implement it with nni
Best AI and Deep learning books to read in 2022
A list of the top books to learn deep learning divided into four distinct categories. Personal reviews are included for each one of them.
3D Medical image segmentation with transformers tutorial
Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset
Spiking Neural Networks: where neuroscience meets artificial intelligence
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