Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mmlouamri/awesome
This repository is a collection of interesting technical resources that I have found on the internet.
https://github.com/mmlouamri/awesome
List: awesome
Last synced: 16 days ago
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This repository is a collection of interesting technical resources that I have found on the internet.
- Host: GitHub
- URL: https://github.com/mmlouamri/awesome
- Owner: mmlouamri
- Created: 2023-03-14T19:20:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-19T09:18:48.000Z (almost 2 years ago)
- Last Synced: 2024-04-10T15:57:35.611Z (9 months ago)
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome - This repository is a collection of interesting technical resources that I have found on the internet. (Other Lists / PowerShell Lists)
README
# AWESOME
This repository is a collection of interesting technical resources that I have found on the internet. The resources are categorized and listed in the `README.md` file for easy reference.
> If you have come across any interesting technical resources that you think would be a great addition to this repository, please don't hesitate to share them with me 🙏! Whether it's a helpful blog post, an informative video, or a useful library or tool.
## Content
- [Academia](#academia)
- [Artificial Intelligence](#artificial-intelligence)
- [Computer Science](#computer-science)
- [Mathematics](#mathematics)
- [Quantum](#quantum)
- [Physics](#physics)
- [Web Dev](#web)## Academia
- [A Survival Guide to a PhD](https://karpathy.github.io/2016/09/07/phd/)
## Artificial Intelligence
- [Deep Learning with PyTorch: A 60min Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)
- [The Neural Network Zoo](https://www.asimovinstitute.org/neural-network-zoo/)- [#258 | Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Lex Fridman Podcast](https://open.spotify.com/episode/6NJt7waroZKSbkt9sZlD7I)
- [MIT 6.S191 - Introduction to Deep Learning](http://introtodeeplearning.com/)
- [Why Neural Networks can learn (almost) anything](https://www.youtube.com/watch?v=0QczhVg5HaI)
- [Deep Learning Basics: Introduction and Overview by Lex Fridman](https://www.youtube.com/watch?v=O5xeyoRL95U)
- [Comment ces IA inventent-elles des images ? (Stable Diffusion, Midjourney, and DALL-E)](https://www.youtube.com/watch?v=tdelUss-5hY)
- [Supervised Learning](https://youtu.be/tdelUss-5hY?t=140)
- [Generative Adversial Nets (GANs)](https://youtu.be/tdelUss-5hY?t=450)
- [Supervised vs Unsupervised learning](https://youtu.be/tdelUss-5hY?t=644)
- [Diffusion models](https://youtu.be/tdelUss-5hY?t=920)- [Comment les I.A. font-elles pour comprendre notre langue ?](https://www.youtube.com/watch?v=CsQNF9s78Nc)
- [Supervised Learning](https://youtu.be/CsQNF9s78Nc?t=70)
- [Convolutional Neural Networks (CNN)](https://youtu.be/CsQNF9s78Nc?t=200)
- [Natural Language Processing](https://youtu.be/CsQNF9s78Nc?t=415)
- [Word Embedding](https://youtu.be/CsQNF9s78Nc?t=554)
- [Recurrent Neural Networks (RNN)](https://youtu.be/CsQNF9s78Nc?t=885)
- [Classification (e.g: Sentiment Analysis)](https://youtu.be/CsQNF9s78Nc?t=1080)
- [Translation](https://youtu.be/CsQNF9s78Nc?t=1130)
- [Memory issue (LSTM, GRU, Attention, Transformers)](https://youtu.be/CsQNF9s78Nc?t=1275)## Computer Science
- [15 Sorting Algorithms in 6 Minutes](https://www.youtube.com/watch?v=kPRA0W1kECg)
- [8 Design Patterns EVERY Developer Should Know](https://www.youtube.com/watch?v=tAuRQs_d9F8)
- [Factory](https://youtu.be/tAuRQs_d9F8?t=45)
- [Builder](https://youtu.be/tAuRQs_d9F8?t=95)
- [Singleton](https://youtu.be/tAuRQs_d9F8?t=143)
- [Observer](https://youtu.be/tAuRQs_d9F8?t=218)
- [Iterator](https://youtu.be/tAuRQs_d9F8?t=312)
- [Strategy](https://youtu.be/tAuRQs_d9F8?t=388)
- [Adapter](https://youtu.be/tAuRQs_d9F8?t=438)
- [Facade](https://youtu.be/tAuRQs_d9F8?t=502)## Mathematics
## Quantum
- [Introduction to Quantum Computing : William Oliver - MIT](https://www.youtube.com/watch?v=lK6fC8E1XPE)
- [Computing Development Timeline](https://youtu.be/lK6fC8E1XPE?t=170)
- [How is a Quantum Computer Different?](https://youtu.be/lK6fC8E1XPE?t=400)
- [Quantum Algorithm](https://youtu.be/lK6fC8E1XPE?t=920)
- [Superconducting qubit](https://youtu.be/lK6fC8E1XPE?t=1445)
- [Quantum Errror Correction analogy with Lacrosse](https://youtu.be/lK6fC8E1XPE?t=2045)
- [The Story of Shor's Algorithm, Straight From the Source | Peter Shor](https://www.youtube.com/watch?v=6qD9XElTpCE)
- [Simon's, discrete log, and factoring problems](https://youtu.be/6qD9XElTpCE?t=480)
- [Error correction](https://youtu.be/6qD9XElTpCE?t=1115)
- [Quantum error correction](https://youtu.be/6qD9XElTpCE?t=1345)## Physics
- [Les inégalités de BELL & les expériences d'Alain ASPECT](https://www.youtube.com/watch?v=28UN70790Do)
- [Intro to Special Relativity Course](https://www.youtube.com/playlist?list=PLoaVOjvkzQtyjhV55wZcdicAz5KexgKvm)
## Web
- [What the heck is the event loop anyway? | Philip Roberts | JSConf EU](https://www.youtube.com/watch?v=8aGhZQkoFbQ)