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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of all the Awesome --Topic Name-- lists I've found till date relevant to Data lifecycle, ML and DL.
| Date | Stars |
|---|---|
| 2026-07-24 | 345 |
| 2026-07-25 | 345 |
| 2026-07-28 | 345 |
| 2026-07-30 | 345 |
| 2026-08-06 | 345 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
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growth rate 0.00%/day
# An Awesome List of Awesomes
<p align="center">
<img width="300" src="https://i.imgur.com/Ky2jxnj.png" "Awesome!">
</p>
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
This is a simple aggregation of all of the "Awesome --Topic name--" github repos I've found till date and I feel are important to get started in the corresponding Topic.
The topics are relevant to Data lifecycle, Machine Learning, Deep learning research and some distributed computing.
Note: Not all of these links are actively maintained but some of them may serve as good starting points.
There are multiple lists for certain topics which may or may not have common links, I have added them with a serial number under the topic in no particular order.
# Topic wise ML and DL research
* [Math](https://github.com/rossant/awesome-math)
* Data augmentation
* [Data Augmentation link 1](https://github.com/CrazyVertigo/awesome-data-augmentation)
* [Data Augmentation link 2](https://brunokrinski.github.io/awesome-data-augmentation/)
* [Data Augmentation review](https://github.com/AgaMiko/data-augmentation-review)
* [Multitask learning](https://github.com/SimonVandenhende/Awesome-Multi-Task-Learning)
* Diffusion models
* [Diffusion models link 1](https://github.com/heejkoo/Awesome-Diffusion-Models)
* [Diffusion models link 2](https://github.com/hyungkwonko/awesome-diffusion-models)
* [Stable Diffusion](https://github.com/awesome-stable-diffusion/awesome-stable-diffusion)
* Self supervised learning
* [Self supervised learning link 1](https://github.com/jason718/awesome-self-supervised-learning)
* [Self supervised learning link 2](https://github.com/wvangansbeke/Self-Supervised-Learning-Overview)
* [Semi supervised learning](https://github.com/yassouali/awesome-semi-supervised-learning)
* Weakly Supervised Learning
* [Weak Supervision](https://github.com/JieyuZ2/Awesome-Weak-Supervision)
* [Weakly Supervised Image Segmentation link 1](https://github.com/gyguo/awesome-weakly-supervised-semantic-segmentation-image)
* [Weakly Supervised Image Segmentation link 2](https://github.com/YimingCuiCuiCui/awesome-weakly-supervised-segmentation)
* [Learning with Label noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise)
* Adversarial ML/DL
* [Adversarial ML](https://github.com/yenchenlin/awesome-adversarial-machine-learning)
* [Adversarial Examples for Deep learning](https://github.com/chbrian/awesome-adversarial-examples-dl)
* [Architecture Search](https://github.com/markdtw/awesome-architecture-search)
* [Contrastive self supervised learning](https://github.com/asheeshcric/awesome-contrastive-self-supervised-learning)
* Zero shot learning
* [Zero shot learning link 1](https://github.com/sbharadwajj/awesome-zero-shot-learning)
* [Zero shot learning link 2](https://github.com/WilliamYi96/Awesome-Zero-Shot-Learning)
* [One shot learning](https://awesomeopensource.com/projects/one-shot-learning)
* Few shot learning
* [Few shot learning link 1](https://github.com/e-271/awesome-few-shot-learning)
* [Few shot learning link 2](https://github.com/Duan-JM/awesome-papers-fewshot)
* [Siamese networks](https://awesomeopensource.com/projects/siamese-network)
* [Image Classification](https://github.com/weiaicunzai/awesome-image-classification)
* [Contrastive learning](https://github.com/VainF/Awesome-Contrastive-Learning)
* [Visual transformers](https://github.com/dk-liang/Awesome-Visual-Transformer)
* [Transformers for vision](https://github.com/lijiaman/awesome-transformer-for-vision)
* [Transformers in Medical Imaging](https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging)
* Transformers
* [Transformers link 1](https://github.com/ictnlp/awesome-transforExcerpt of 42,943 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:58230346886395ad, topic:nlp, topic:natural-language-processing
matched fp:58230346886395ad, topic:deep-learning
matched fp:58230346886395ad, topic:computer-vision, readme:image segmentation, readme:semantic segmentation
matched fp:58230346886395ad, topic:papers, name:awesome list, readme:awesome list