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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.  


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.


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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
Submit Your Assignment 00:00:00
Certification 00:00:00

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