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About Lecture 1
This document provides an overview of Course 6.S094: Deep Learning for Self-Driving Cars at MIT. The course uses deep learning methods like convolutional neural networks, recurrent neural networks, and deep reinforcement learning to teach students how to apply these techniques to problems in autonomous driving like perception, localization, mapping, control, planning and driver monitoring. The course includes two projects - DeepTraffic and DeepTesla - as well as guest lectures. Driving presents complex challenges that require understanding scenes, planning routes, and responding to other drivers, pedestrians and traffic situations. While early autonomous vehicles relied on rules-based programming, modern self-driving cars increasingly use neural networks trained on large amounts of data.
Detailed Information
| Author: | ['Jimmy Dore'] |
|---|---|
| Publication Year: | 2017 |
| Pages: | 82 |
| Language: | English |
| Format: | |
| Price: | FREE |
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