Self-driving cars

This is probably the coolest and most promising application of deep learning. An autonomous vehicle has a number of cameras attached to it. The output video stream is fed into deep learning networks that recognize, as well as segment, different objects present around the car. NVIDIA has introduced an End to End Learning for Self-Driving Cars, which is a convolutional neural network that takes in input images from cameras and predicts what actions should be taken in the form of steering angle or acceleration. For training the network, the steering angles and throttle and camera views are stored when a human is driving along, documenting the actions taken by him for the changes occurring around him. The network's parameters are then updated through backpropagating (Backpropagation is discussed in details in Chapter 2, Deep Feedforward Networks) the error from human input and the network's predictions.