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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.
Applied Deep Learning Course
| Date | Stars |
|---|---|
| 2026-07-31 | 3553 |
| 2026-08-01 | 3553 |
| 2026-08-03 | 3553 |
| 2026-08-06 | 3556 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# [Applied Deep Learning](https://arxiv.org/pdf/2301.11316.pdf) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4))
@article{raissi2023open,
title={Open Problems in Applied Deep Learning},
author={Raissi, Maziar},
journal={arXiv preprint arXiv:2301.11316},
year={2023},
url = {https://arxiv.org/pdf/2301.11316.pdf}
}
## Course Objectives & Prerequisites:
This is a two-semester-long course primarily designed for graduate students. However, undergraduate students with demonstrated strong backgrounds in probability, statistics (e.g., linear & logistic regressions), numerical linear algebra and optimization are also welcome to register. We will be pursuing the objective of familiarizing the students with state-of-the-art deep learning techniques employed in the industry. Deep learning is a field that has been witnessing a mini-revolution every few months. It is therefore very important that the students registering for this course are eager to learn new concepts. So much of deep learning is just software engineering. Consequently, the students should be able to write clean code while doing their assignments. Python will be the programming language used in this course. Familiarity with TensorFlow and PyTorch is a plus but is not a requirement. However, it is very important that the students are willing to do the hard work to learn and use these two frameworks as the course progresses.
## Part I Topics (Fall Semester)
* Training Deep Neural Networks ([Lecture Notes](00%20-%20Training.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNmrvBG-zFov1K5w3SWGlx1r))
* Computer Vision
* Image Classification
* Large Networks ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/01%20-%20Large%20Networks.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNlO37YBuFDU7_meLSjvSw2a))
* Small Networks ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/02%20-%20Small%20Networks.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNk2TXiWDl0i-Gsou3ejD7za))
* AutoML ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/03%20-%20AutoML.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNnQXiups8QMzmyKe4b3ge6F))
* Robustness ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/04%20-%20Robustness.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNnBnw17OkCP70ASwmHQqvQt))
* Visualizing & Understanding ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/05%20-%20Visualizing%20%26%20Understanding.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNkOG-DEF1RQ_oPXKGTO988o))
* Transfer Learning ([Lecture Notes](01%20-%20Computer%20Vision/01%20-%20Image%20Classification/06%20-%20Transfer%20Learning.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNkn3ofeGWkuLllzdbVPMqWS))
* Image Transformation
* Semantic Segmentation ([Lecture Notes](01%20-%20Computer%20Vision/02%20-%20Image%20Transformation/01%20-%20Semantic%20Segmentation.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNkh9JAoKa9TwO2LufHYdQSz))
* Super-Resolution, Denoising, and Colorization ([Lecture Notes](01%20-%20Computer%20Vision/02%20-%20Image%20Transformation/02%20-%20Super-Resolution%2C%20Denoising%2C%20and%20Colorization.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNl6RQNFrHnxNWzq3Byy8bAd))
* Pose Estimation ([Lecture Notes](01%20-%20Computer%20Vision/02%20-%20Image%20Transformation/03%20-%20Pose%20Estimation.pdf)) ([YouTube Playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNmPGaVQYDWydc2ZmEt6zdrV))
* Optical Flow and Depth Estimation ([Lecture Notes](01%20-%20Computer%20Vision/02%20-%20Image%Excerpt of 51,553 characters
Read on GitHub
Maziar Raissi
384
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5213f9e468a2d596, desc:course