It's a notebook of Self-Driving Cars which is instructed by Prof. Dr.-Ing. Andreas Geiger in 2021.
Class link: youtube.
Course Website with Slides, Lecture Notes, Problems and Solutions.
https://gym.openai.com/envs/CarRacing-v0/
- Literature: (links to papers in footnote
- Janai et al.: Computer Vision for Autonomous Vehicles https://arxiv.org/abs/1704.05519
- Szeliski: Computer Vision: Algorithms and Applications https://szeliski.org/Book/
- Goodfellow, Bengio, Courville: Deep Learning http://www.deeplearningbook.org
- Talks, Courses and Tutorials:
- Stachniss (Bonn): Self-Driving Cars https://www.ipb.uni-bonn.de/sdc-2020/
- Karpathy (Tesla): Tesla AI Day https://www.youtube.com/watch?v=j0z4FweCy4M
- Fridman (MIT): Self-Driving Cars https://deeplearning.mit.edu/
- Urtasun (UoT): All about SD http://www.allaboutselfdriving.com/
- The Python Tutorial https://docs.python.org/3/tutorial/
- NumPy Quickstart https://numpy.org/devdocs/user/quickstart.html
- PyTorch Tutorial https://pytorch.org/tutorials/
- Latex / Overleaf Tutorial https://www.overleaf.com/learn
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Basic math skills
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Linear algebra, probability and information theory. If unsure, have a look at: Goodfellow et al.: Deep Learning (Book), Chapters 1-4 Luxburg: Mathematics for Machine Learning (Lecture) Deisenroth et al.: Mathematics for Machine Learning (Book)
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Basic computer science skills
- Variables, functions, loops, classes, algorithms
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Experience with deep learning. If unsure, take a deep learning course:
- Geiger: Deep Learning (Lecture)
- Basic Python and PyTorch coding skills https://docs.python.org/3/tutorial/ https://pytorch.org/tutorials/