Top AI Repos — open-source AI, indexed and scored
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 list of AI Art courses, tools, libraries, people, and places.
| Date | Stars |
|---|---|
| 2026-07-24 | 364 |
| 2026-07-25 | 364 |
| 2026-07-28 | 364 |
| 2026-07-30 | 364 |
| 2026-08-06 | 364 |
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[](https://teia.art/objkt/538875) > Resources at the intersection of AI _AND_ Art. Mainly tools and tutorials but also with some inspiring people and places thrown in too! For a broader resource covering more general creative coding tools (that you might want to use with what is listed here), check out [terkelg/awesome-creative-coding](https://github.com/terkelg/awesome-creative-coding) or [thatcreativecode.page](https://thatcreativecode.page/). For resources on AI and deep learning in general, check out [ChristosChristofidis/awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning) and [https://github.com/dair-ai](https://github.com/dair-ai). ## Contents * [Learning](#learning) * [Courses](#courses) * [Videos](#videos) * [Books](#books) * [Tutorials and Blogs](#tutorials-and-blogs) * [Papers/Methods](#papers-methods) * [Diffusion models (and text-to-image)](#diffusion-models-and-text-to-image) * [Neural Radiance fields (and NeRF like things)](#neural-radiance-fields-and-nerf-like-things) * [3D and point clouds](#3d-and-point-clouds) * [Unconditional Image Synthesis](#unconditional-image-synthesis) * [Conditional Image Synthesis (and inverse problems)](#conditional-image-synthesis-and-inverse-problems) * [GAN inversion (and editing)](#gan-inversion-and-editing) * [Latent Space Interpretation](#latent-space-interpretation) * [Image Matting](#image-matting) * [Tools](#tools) * [Creative ML](#creative-ml) * [Deep Learning](#deep-learning-frameworks) * [Runtimes/Deployment](#runtimesdeployment) * [text-to-image](#text-to-image) * [Creative Coding](#creative-coding) * [Stable Diffusion](#stable-diffusion-sd) * [Datasets](#datasets) * [Products/Apps](#productsapps) * [Artists](#artists) * [Institutions/Places](#institutionsplaces) * [Related Lists](#related-lists-and-collections) > __bold__ entries signify my favorite resource(s) for that section/subsection (if I _HAD_ to choose a single resource). Additionally each subsection is usually ordered by specificity of content (most general listed first). ## Learning ### Courses #### General Deep Learning * [Practical Deep Learning for Coders (fast.ai)](https://course19.fast.ai/index.html) * [Deep Learning (NYU)](https://atcold.github.io/pytorch-Deep-Learning/) * [Introduction to Deep Learning (CMU)](https://deeplearning.cs.cmu.edu/F22/resources.html) * ⭐️ __[Deep Learning for Computer Vision (UMich)](https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/)__ * [Deep Learning for Computer Vision (Stanford CS231n)](http://cs231n.stanford.edu/index.html) * [Natural Language Processing with Deep Learning (Stanford CS224n)](https://web.stanford.edu/class/cs224n/) #### Deep Generative Modeling * [Deep Generative Models (Stanford)](https://deepgenerativemodels.github.io/) * [Deep Unsupervised Learning (UC Berkeley)](https://sites.google.com/view/berkeley-cs294-158-sp20/home) * [Differentiable Inference and Generative Models (Toronto)](http://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html) * ⭐️ __[Learning-Based Image Synthesis (CMU)](https://learning-image-synthesis.github.io/sp22/)__ * [Learning Discrete Latent Structure (Toronto)](https://duvenaud.github.io/learn-discrete/) * [From Deep Learning Foundations to Stable Diffusion (fast.ai)](https://www.fast.ai/posts/part2-2022.html) #### Creative Coding and New Media * ⭐️ __[Deep Learning for Art, Aesthetics, and Creativity (MIT)](https://ali-design.github.io/deepcreativity/)__ * [Machine Learning for the Web (ITP/NYU)](https://github.com/yining1023/machine-learning-for-the-web) * [Art and Machine Learning (CMU)](https://sites.google.com/site/artml2018/lectures) * [New Media Installation: Art that Learns (CMU)](https://artthatlearns.wordpress.com/syllabus/) * Introduction to Computational Media (ITP/NYU) * [Media course](https://github.com/ITPNYU/ICM-2022-Media) * [Code course]
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:703b58a205e88cbc, topic:deep-learning, topic:pytorch, topic:tensorflow
matched fp:703b58a205e88cbc, topic:text-to-image, topic:generative-art, readme:text-to-image
matched fp:703b58a205e88cbc, topic:awesome, topic:awesome-list, desc:a list of