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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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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This video is about how to predict the stock price of a company using a recurrent neural network. We will learn how to create our features and label and how to create a recurrent neural network using Keras.
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