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awesome-ai-tutorials

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The best collection of AI tutorials to make you a boss of Data Science!

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# 😎 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.

![Awesome](https://img.shields.io/badge/Awesome-YES-brightgreen) ![AI](https://img.shields.io/badge/AI-Tutorials-blue)

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](https://medium.com/@francoisporcher).

# 📚 Content

## 📷 Computer Vision

### 🖼️ Image Segmentation
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [Cooking your first Unet](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/Computer%20Vision/UNet)
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [V-Net, U-Net’s big brother in Image Segmentation](https://medium.com/@francoisporcher/v-net-u-nets-big-brother-in-image-segmentation-906e393968f7)
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [The Ultimate guide to nnU-Net for State Of the Art Image Segmentation](https://medium.com/@francoisporcher/the-ultimate-guide-to-nnu-net-for-state-of-the-art-image-segmentation-6dda7f44b935), [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/004%20-%20nnU-Net)
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [Entropy-Based Uncertainty in Image Segmentation](https://medium.com/towards-data-science/entropy-based-uncertainty-prediction-812cca769d7a) [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/006%20-%20Entropy%20based%20uncertainty%20for%20Image%20Segmentation)

### Vision Transformers (ViT)

- ![NEW 💥](https://img.shields.io/badge/NEW-💥-red) ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [The Ultimate Guide to Vision Transformers](https://medium.com/towards-data-science/the-ultimate-guide-to-vision-transformers-0a6df32cb248)
- ![NEW 💥](https://img.shields.io/badge/NEW-💥-red) ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [How to Train a Vision Transformer (ViT) from scratch?](https://medium.com/towards-data-science/)
- ![NEW 💥](https://img.shields.io/badge/NEW-💥-red) ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [The Ultimate Guide to Masked Autoencoders (MAE)](https://medium.com/ai-advances/the-ultimate-guide-to-masked-autoencoders-mae-87aa6883ff47)
- ![Incoming](https://img.shields.io/badge/Status-Incoming-blue) How to Implement State-of-the-Art Masked AutoEncoders (MAE)?

### 🎯 Object Detection
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [DETR: Detection Transformer](https://medium.com/p/a8b3327b737a)

## 📖 Natural Language Processing (NLP)
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [The A-Z of Transformers: Everything You Need to Know](https://medium.com/p/c9f214c619ac) [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/NLP/007%20-%20Transformers%20From%20Scratch)

## 📚 Reinforcement Learning

- ![Incoming](https://img.shields.io/badge/Status-Incoming-blue) k-arms bandit: A Comprehensive Guide on both stationary and non-stationary problems
- ![Incoming](https://img.shields.io/badge/Status-Incoming-blue) Thompson Sampling

## 📊 Bayesian Deep Learning
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [A Gentle Introduction to Bayesian Deep Learning](https://medium.com/@francoisporcher/a-gentle-introduction-to-bayesian-deep-learning-d298c7243fd6)

## ⚡ Efficient Deep Learning
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [A beginner's guide to Quantization](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/Efficient%20Deep%20Learning/Quantization)
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [A beginner's guide to Pruning](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/Efficient%20Deep%20Learning/Pruning)

## ☁️ Cloud Computing

- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [How to Send SLURM Jobs to a Cluster](https://towardsdatascience.com/how-to-send-a-slurm-job-to-a-cluster-dd1cf021c7ac)
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [What is Distributed Data Parallel (DDP)](https://medium.com/towards-data-science/a-comprehensive-guide-of-distributed-data-parallel-ddp-2bb1d8b5edfb) [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/Cloud%20Computing/Distributed%20Data%20Parallel%20(DDP))

s
## 🔧 MLOps
- ![Easy](https://img.shields.io/badge/Difficulty-Easy-green) [How to use Weights and Biases?](https://medium.com/ai-mind-labs/how-to-use-weights-and-biases-c4467c6dac27), [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/MLOps/How%20to%20use%20Weights%20and%20Biases)
- ![NEW 💥](https://img.shields.io/badge/NEW-💥-red) ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [How to test a Machine Learning Project?](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/MLOps/how_to_test), [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/MLOps/how_to_test)

## 🔮 Uncertainty Estimation
- ![Medium](https://img.shields.io/badge/Difficulty-Medium-yellow) [Entropy-Based Uncertainty in Image Segmentation](https://medium.com/towards-data-science/entropy-based-uncertainty-prediction-812cca769d7a) [[Github]](https://github.com/FrancoisPorcher/awesome-ai-tutorials/tree/main/Uncertainty%20Estimation/Entropy%20based%20uncertainty%20for%20Image%20Segmentation)

## Representation Learning

- ![Incoming](https://img.shields.io/badge/Status-Incoming-blue) t-SNE: A Comprehensive Guide

## 👨‍💻 About the Author

Hey! My name is [François](https://www.linkedin.com/in/fporcher/), 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! 🤗

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