AI Summer

How to apply classifier-free guidance (CFG) on your diffusion models without conditioning dropout? What are the newest alternatives to generative sampling with diffusion models? Find out in this article!

Learn more about the nuances of classifier-free guidance, the core sampling mechanism of current state-of-the-art image generative models called diffusion models.

Do you want to learn all the latest state-of-the-art methods of the last year? Learn about the best and most famous papers that made the cut from this year’s ICCV. See the latest trends in AI and computer vision.

Learn about Apache Airflow and how to use it to develop, orchestrate and maintain machine learning and data pipelines

We study the learned visual representations of CNNs and ViTs, such as texture bias, how to learn good representations, the robustness of pretrained models, and finally properties that emerge from trained ViTs.

This blogpost is about starting learning pytorch with a hands on tutorial on image classification.

Explore the basic idea behind neural fields, as well as the two most promising architectures (Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives)

A deep dive into the mathematics and the intuition of diffusion models. Learn how the diffusion process is formulated, how we can guide the diffusion, the main principle behind stable diffusion, and their connections to score-based models.

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.

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

Learn all there is to know about transformer architectures in computer vision, aka ViT.

A mathematical explanation of the Swapping Assignments Between Views (SWAV) paper.

Explore the most popular gnn architectures such as gcn, gat, mpnn, graphsage and temporal graph networks