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Deep learning is a part of Machine Learning and has networks capable of learning unsupervised from data that is unstructured or unlabeled. The [course_title] course covers the practical practice of deep learning through the applied theme of building a self-driving car.
You will learn Deep Reinforcement learning for motion planning, Convolutional Neural Networks for end-to-end learning of the Driving Task, Recurrent Neural Networks for steering through time, and Deep Learning for human-centred Semi-Autonomous vehicles.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: Massachusetts Institute of Technology
Course Curriculum
| Deep Learning for Self-Driving Cars | |||
| Deep Learning | 01:02:00 | ||
| Self-Driving Cars | 01:13:00 | ||
| Deep Reinforcement Learning | 00:58:00 | ||
| Computer Vision | 00:53:00 | ||
| Deep Learning for Human Sensing | 01:12:00 | ||
| Self-Driving Cars | 01:13:00 | ||
| Introduction to Deep Learning and Self-Driving Cars | 01:31:00 | ||
| Deep Reinforcement Learning for Motion Planning | 01:28:00 | ||
| Convolutional Neural Networks for End-to-End Learning of the Driving Task | 01:20:00 | ||
| Recurrent Neural Networks for Steering Through Time | 01:16:00 | ||
| Deep Learning for Human-Centered Semi-Autonomous Vehicles Lex Fridman | 00:34:00 | ||
| Chris Gerdes (Stanford) on Technology, Policy and Vehicle Safety in the Age of AI | 01:00:00 | ||
| Sertac Karaman (MIT) on Motion Planning in a Complex World | 01:02:00 | ||
| Intro to Machine Learning | 01:29:00 | ||
| Self-Driving Cars | 01:07:00 | ||
| Self-Driving Cars | 00:37:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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