Welcome to this AWESOME repository of AI Tutorials!

This is a collection of tutorials I have made on various aspects of Artificial Intelligence (AI). I dig deep into subjects and you will information that you find nowhere else. Whether you're a complete beginner or an experienced AI enthusiast, these tutorials will provide you with valuable insights and practical knowledge and enable you to upskill in Computer Vision, NLP, Uncertainty Prediction, MLOps, Cloud Computing and more.

You can find a list of my articles on Medium.

• Cooking your first Unet
• V-Net, U-Net’s big brother in Image Segmentation
• The Ultimate guide to nnU-Net for State Of the Art Image Segmentation, [Github]
• Entropy-Based Uncertainty in Image Segmentation [Github]

• DETR: Detection Transformer
• CO-DETR [Incoming]

• The A-Z of Transformers: Everything You Need to Know [Github]
• BERT from Scratch [Incoming]
• GPT from scratch [Incoming]
• Mixture of Experts [Incoming]

• A Gentle Introduction to Bayesian Deep Learning

• A beginner's guide to Quantization
• A beginner's guide to Pruning

• How to Send SLURM Jobs to a Cluster
• What is Distributed Data Parallel (DDP) [Github]
• What Fully Sharded Data Parallel (FSDP) [Incoming]

• How to use Weights and Biases?, [Github]

• Entropy-Based Uncertainty in Image Segmentation [Github]
• Conformal Prediction (Incoming)

Hey! My name is François, I am a A.I Research Scientist intern at Meta. Previously I was doing research on Deep Learning and NeuroImaging at Cambridge University.

Being very curious and constantly learning, I have a very broad range of interests. I also like to break down the most complext topics into the tiniest bits and explain them in a simple way.

This is why I decided to distill all this knowledge into a series of articles/tutorials that I hope you will find useful.

Enjoy! 🤗