| The deep learning approach to machine learning emphasizes high-capacity, scalable models that learn distributed representations of their input. This dissertation demonstrates the efficacy and generality of this approach in a series of diverse case studies in speech recognition, computational chemistry, and natural language processing. (Abstract shortened by UMI.). |
