Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning

Bay Area Vision Meeting (more info below) Unsupervised Feature Learning and Deep Learning Presented by Andrew Ng March 7, 2011 ABSTRACT Despite machine learning’s numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed “unsupervised feature learning“ and “deep learning“ algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature representation. These methods have also surpassed the previous state-of-the-art on a number of problems in vision, audio, and t