Andreu Sancho Homepage

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...

Today’s mid- and high-end computers come with a tremendous hardware, mostly used in video games and other media software, that can be exploited for advanced computation, that is: High Performance Computing (HPC). This is a hot topic in Deep Learning as modern graphic cards come with huge streaming process power and large and quick memory. The most successful example is in Nvidia’s CUDA platform. In summary, CUDA significantly speeds up the fitting of large neural nets (for instance: from several hours to just a few minutes!). However, the drawbacks come when setting up the scenario: it is non-trivial to install the requirements and set it running, and personally I had a little trouble the first time as many packages need to be manually compiled and installed in a specific order. The purpose of this entry is to reflect what I did for setting up Theano and Keras with HPC using an Nvidia’s graphic card (in my case a GT730) using GNU/Linux. To do so, I will start assuming a clean Debi...

Neural nets, with their flexible data representation capable of approximate any arbitrary function, are in a new renaissance with the development of Deep Leaners. These stack many layers (typically dozens of quasi-independent layers) of traditional neurons to achieve something very remarkable: self detection of important features from input data. The application of Deep Learning is not restricted to raw classification or regression; these family of techniques are applied to much broader fields such as machine vision and speech recognition. Recent advances went much further and combined a Deep Learning architecture with a Reinforcement Learning algorithm generating a computer program capable of beating classic Atari video games without being explicitly programmed for this task, scoring as a top human player. It is not surprising, therefore, that they appear in the last Nature’s cover (vol. 518, Num. 7540, pp. 456-568) . But how do they work? First, one must take a close look to the hum...