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