Deep Learning and Self-Driving Cars from MIT: Lectures 01-05

Deep Learning and Self-Driving Cars from MIT: Lectures 01-05

It doesn’t matter if you are beginner or new to machine learning or advanced researcher in the field of deep learning methods and their application, everybody can benefit of Lex Fridman’s course on Deep Learning for Self-Driving Cars.

Lecture 1:
Introduction to Deep Learning and Self-Driving Cars

*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

Lecture 2:
Deep Reinforcement Learning for Motion Planning

This lecture introduces types of machine learning, the neuron as a computational building block for neural nets, q-learning, deep reinforcement learning, and the DeepTraffic simulation that utilizes deep reinforcement learning for the motion planning task.

Lecture 3:
Convolutional Neural Networks for End-to-End Learning of the Driving Task

This lecture introduces computer vision, convolutional neural networks, and end-to-end learning of the driving task.

Lecture 4:
Recurrent Neural Networks for Steering through Time

This lecture introduces “Recurrent Neural Networks for Steering Through Time”..

Lecture 5:
Deep Learning for Human-Centered Semi-Autonomous Vehicles

This lecture introduces “Deep Learning for Human-Centered Semi-Autonomous Vehicles”.

Lex Fridman is a Postdoctoral Associate at the MIT AgeLab. He received his BS, MS, and PhD from Drexel University where he worked on applications of machine learning and numerical optimization techniques in a number of fields including robotics, ad hoc wireless networks, active authentication, and activity recognition. Before joining MIT, Dr. Fridman worked as a visiting researcher at Google. His research interests include machine learning, decision fusion, developing and applying deep neural networks in the context of driver state sensing, scene perception, and shared control of semi-autonomous vehicles and numerical optimization.

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