Machine learning is a very successful technology, but applying it to a new problem usually means spending a long time hand-designing the input features to feed to the learning algorithm. This is true for applications in vision, audio, and text/NLP. To address this, researchers in machine learning have recently developed “deep learning” algorithms, which can automatically learn feature representations from unlabeled data, thus bypassing most of this time-consuming engineering. These algorithms are based on building massive artificial neural networks, that were loosely inspired by cortical (brain) computations. In this talk, I describe the key ideas behind deep learning, and also discuss the computational challenges of getting these algorithms to work. I’ll also present a few case studies, and report on the results from a project that I led at Google to build massive deep learning algorithms, resulting in a highly distributed neural network trained on 16,000 CPU cores, and that learned by itself to discover high level concepts such as common objects in video.
Published on Jun 17, 2013
Andrew Ng of Stanford University, Technion lecture: Machine Learning via Large-scale Brain Simulations
(Source: YouTube | Technion)