> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/machine-learning/machine-learning-sota.md).

# Machine Learning SOTA and Model Zoo

## List of SOTA

* [State of the Art AI](https://www.stateoftheart.ai/)
* [Gradio Hub](https://gradiohub.com/)
  * Sketch Recognition
  * Painting Generator (StyleGAN)
  * Pose Estimation
  * Colorize Photos (DeOldify)
  * Identifying Skin Cancer
  * Emotion Classification
  * MobileNet vs. InceptionNet
* [Papers with Code SOTA](https://paperswithcode.com/sota)
* [AI Hub by Google](https://aihub.cloud.google.com/s?category=notebook)

## Model Zoo

* [Model Zoo](https://modelzoo.dev/) Machine Learning Playground
* [Cloud Blobcity](https://cloud.blobcity.com/#/ps/explore) : GitHub Data Science projects repository, executable in 1 click
* [CKnowledge](https://cknowledge.io/) Find portable workflows, reusable artifacts and automation actions for deep tech (AI, ML, quantum, systems...), ML architecture comparison
* [Gradio](https://github.com/gradio-app/gradio)
* [Gradio Spaces](https://huggingface.co/spaces)
* [black0017/MedicalZooPytorch: A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation](https://github.com/black0017/MedicalZooPytorch)
* [CatalyzeX](https://www.catalyzex.com/) : Discover AI models & code to catalyze your projects

## Report about AI Progress

* [State of AI](https://www.stateof.ai/)
* [AI Index](https://hai.stanford.edu/research/ai-index-2019)
* [EFF AI Metrics](https://www.eff.org/ai/metrics)

## Specific SOTA

* [SOTA for Music Separation](https://paperswithcode.com/sota/music-source-separation-on-musdb18)
* [State-of-the-Art Image Generative Models–Aran Komatsuzaki](https://arankomatsuzaki.wordpress.com/2021/03/04/state-of-the-art-image-generative-models/)

## Machine Learning Review

* [Best deep CNN architectures and their principles: from AlexNet to EfficientNet - AI Summer](https://theaisummer.com/cnn-architectures/)