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Showing posts from January, 2017

A conceptual machine for cloning a human driver

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If anything else, a Machine Learning practitioner has to get a global view and think on how far our conceptual machines can go. This is a little tale of my recent experience with Deep Learning. To start with, say that I am enrolled in the Udacity’s nanodegree inSelf Driving Car Engineer . Here we are learning the techniques needed for building a car controller capable of driving at the human level. The interesting part of the course (to be read as where one truly learns) is in the so called projects: these are nothing but assignments in which the student has to solve a challenging task, mainly by using Convolutional Neural Networks (convnets), the most well-known Deep Learning models. So far, the most mind-blowing task and the focus of this entry is project 3, were we have to record our driving behavior in a car simulator and let a convnet to clone it and successfully generalize the act of driving. It is remarkable that a convnet learns to control the car from raw color images jointly