README.md

😎 Awesome AI Tutorials

Welcome to this AWESOME repository of AI Tutorials! I am a Research Scientist at Meta and I have created this repository to share my learnings with the community.

If you are looking for an all-in-one place to learn about the latest trends in AI with high quality explanations/code, you are in the right place!

This repository contains a series of tutorials on various topics in AI, from Computer Vision to Natural Language Processing, Reinforcement Learning, Uncertainty Prediction, MLOps, Cloud Computing and more.

You can find a list of my articles on Medium.

📚 Content

📷 Computer Vision

🖼️ Image Segmentation

• 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]

Vision Transformers (ViT)

• The Ultimate Guide to Vision Transformers
• How to Train a Vision Transformer (ViT) from scratch?
• The Ultimate Guide to Masked Autoencoders (MAE)
• How to Implement State-of-the-Art Masked AutoEncoders (MAE)?

🎯 Object Detection

• DETR: Detection Transformer

📖 Natural Language Processing (NLP)

• The A-Z of Transformers: Everything You Need to Know [Github]

📚 Reinforcement Learning

• k-arms bandit: A Comprehensive Guide on both stationary and non-stationary problems
• Thompson Sampling

📊 Bayesian Deep Learning

• A Gentle Introduction to Bayesian Deep Learning

⚡ Efficient Deep Learning

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

☁️ Cloud Computing

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

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🔧 MLOps

• How to use Weights and Biases?, [Github]
• How to test a Machine Learning Project?, [Github]

🔮 Uncertainty Estimation

• Entropy-Based Uncertainty in Image Segmentation [Github]

Representation Learning

• t-SNE: A Comprehensive Guide

👨‍💻 About the Author

Hey! My name is François, I am a A.I Research Scientist at Meta. Previously I was also doing research in Computer vision and LLMs at Stanford University and Cambridge University.

Being very curious and constantly learning, I have a very broad range of interests. I also like to break down the most complex 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! 🤗