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Lex Fridman:
fridman@mit.edu
Website:
cars.mit.edu
January
2017
Course 6.S094:
Deep Learning for Self-Driving Cars
6.S094: Deep Learning for Self-Driving Cars
Lecture 1: Introduction to Deep Learning and Self-Driving Cars
cars.mit.edu
References: [13, 14]
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Lex Fridman:
fridman@mit.edu
Website:
cars.mit.edu
January
2017
Course 6.S094:
Deep Learning for Self-Driving Cars
Administrative
• Website: cars.mit.edu
• Contact Email: deepcars@mit.edu
• Required:
• Create an account on the website.
• Follow the tutorial for each of the 2 projects.
• Recommended:
• Ask questions
• Win competition!
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Lex Fridman:
fridman@mit.edu
Website:
cars.mit.edu
January
2017
Course 6.S094:
Deep Learning for Self-Driving Cars
Target Audience
You may be:
• New to programming
• New to machine learning
• New to robotics
What you will learn:
• An overview of deep learning methods:
• Deep Reinforcement Learning
• Convolutional Neural Networks
• Recurrent Neural Networks
• How deep learning can help improve each component of
autonomous driving: perception, localization, mapping, control,
planning, driver state
![](https://csdnimg.cn/release/download_crawler_static/89540790/bg4.jpg)
Lex Fridman:
fridman@mit.edu
Website:
cars.mit.edu
January
2017
Course 6.S094:
Deep Learning for Self-Driving Cars
Target Audience
Not many equation slides like the following:
* Though it would be more efficient, since the above is LaTeX code automatically
generated character by character with Recurrent Neural Networks (RNNs)
[35] Andrej Karpathy. “The Unreasonable Effectiveness of Recurrent Neural Networks." (2015).
References: [35]
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Lex Fridman:
fridman@mit.edu
Website:
cars.mit.edu
January
2017
Course 6.S094:
Deep Learning for Self-Driving Cars
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