Everything you need to know about Transformers, and how to implement them — Why another tutorial on Transformers? You have probably already heard of Transformers, and everyone talks about it, so why making a new article about it? Well, I am a researcher, and this requires me to have a very deep understanding of the tools I use (because if you don’t understand them, how can you identify…

Deep Dive and clear explanations on the paper “End to end detection with transformers” — Note: This article delves into the intricate world of Computer Vision, specifically focusing on Transformers and the Attention Mechanism. It’s recommended to be acquainted with the key concepts from the paper “Attention is All You Need.” A Snapshot of History DETR, short for DEtection TRansformer, pioneered a novel wave in object detection upon its…

Everything you need to know to understand the State of the Art nnU-Net, and how to apply it to your own dataset. — During my Research internship in Deep Learning and Neurosciences at Cambridge University, I used the nnU-Net a lot, which is an extremely strong baseline in Semantic Image Segmentation. However, I struggled a little to fully understand the model and how to train it, and did not find so much help…

Welcome to the exciting world of Probabilistic Programming! This article is a gentle introduction to the field, you only need a basic understanding of Deep Learning and Bayesian statistics. — By the end of this article, you should have a basic understanding of the field, its applications, and how it differs from more traditional deep learning methods. If, like me, you have heard of Bayesian Deep Learning, and you guess it involves bayesian statistics, but you don't know exactly how…

This article explores how Entropy can be employed as a tool for uncertainty estimation in image segmentation tasks. We will walk through what Entropy is, and how to implement it with Python. — While working at Cambridge University as a Research Scientist in Neuroimaging and AI, I faced the challenge of performing image segmentation on intricate brain datasets using the latest Deep Learning techniques, especially the nnU-Net. During this endeavor, I observed a significant gap: the overlooking of uncertainty estimation. Yet, uncertainty is…

A full tutorial on how to track your data science experiments with W&B — Imagine a workday where you tweak the learning rate, or perhaps experiment with a new architecture. On another day, you try a novel data augmentation technique. Fast forward a bit, and you’re adding more images to the mix. Before you know it, you’re swimming in a sea of experiments —…

A tutorial on how to send SLURM jobs to a cluster, especially for deep learning and data science — So you are used to train Deep Learning models with the free GPUs of Google Colab, but you are ready to level up and harness the power of a cluster, and you have no idea how to do that? You’re in the right place! 🚀 During my Research internship in…

Welcome to this guide about the V-Net, the cousin of the well known U-Net, for 3D images segmentations. You will know it inside out! — Welcome to an exciting journey through the world of deep learning architectures! You may already be familiar with U-Net, a game-changer in computer vision that has significantly reshaped the landscape of image segmentation. Today, let’s turn the spotlight onto U-Net’s big brother, the V-Net.

This article goes hand in hand with my previous article on Quantization, in the theme of Model Compression in Deep Learning. In this article, we will demystify ‘Pruning’ and guide you through the process of pruning models from scratch. …