A look at deep learning for science

Check out Fundamentals of Deep Learning by Nikhil Buduma to learn about key concepts in this complex and exciting field. Deep learning is enjoying unprecedented success in a wide variety of commercial applications. Around 10 years ago, very few practitioners could have predicted that deep learning-powered systems would surpass human-level performance in computer vision and speech recognition tasks. At Lawrence Berkeley National Laboratory, we are confronted with some of the most challenging data analytics problems in science. While there are similarities between commercial and scientific applications in terms of the overall analytics tasks (classification, clustering, anomaly detection, etc.), a priori, there is no reason to believe that the underlying complexity of scientific data sets would be comparable to ImageNet. Are deep learning methods powerful enough to produce state-of-the-art performance for scientific analytics tasks?