MIT 6.S094: Convolutional Neural Networks for End-to-End Learning of the Driving Task

This is lecture 3 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. This lecture introduces computer vision, convolutional neural networks, and end-to-end learning of the driving task.


Links to individual lecture videos for the course:

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

Lecture 2: Deep Reinforcement Learning for Motion Planning

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

Lecture 4: Recurrent Neural Networks for Steering through Time

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

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►KERAS Course –

Hey guys and welcome to another fun and easy machine tutorial on Convolutional Neural Networks.

What are Convolutional Neural Networks and why are they important?

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars.

A ConvNet is able to recognize scenes and the system is able to suggest relevant captions for example (“a girl playing tennis”) while this image shows an example of ConvNets being used for recognizing everyday objects, humans and animals. There are also ConvNets involved in playing games like StarCraft, Mario and Doom.

ConvNets, therefore, are an important tool for most deep learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be a bit daunting. But don’t worry you are in good hands here at Arduino Startups. If you are new to neural networks in general, I would recommend you check out my lecture on Artificial Neural Networks and then return to this one.

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32 Antworten auf „MIT 6.S094: Convolutional Neural Networks for End-to-End Learning of the Driving Task“

  1. My final project at school is autonomous driving with CNN. I have a problem with softmax function. I should take 3 steering output (right, left, straight) at the end of the CNN however how should CNN calculate steering angle values. Right,letf,straight do not mean anything if i dont know how much steering value. My training set outputs (steering values) changing between -1 and 1.

    Note: Softmax function do not accept float values. It just accept integer and booleen.

  2. We live in incredible times that these lectures are available online. This information is crucial for my work and I have no idea how I'd be able to educate myself if I didn't have access to these lectures. I really appreciate your work.

  3. Is the Convolution layer a separate Neural Network? Why are we calling it a layer? How do we pick the values of the convolutional filter? if arbitrary – are they adjusted somehow? Is Backpropagation happening only on the fully connected layer which is a separate neural Network? I thought Backpropagation can only be done with one Input layer and one output layer.

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