talk_title---->(字串長度:0)---->Structure Learning in Deep Learning(字串長度:35)```desc---->(字串長度:0)---->The goal of data science is to extract insights from unstructured and complex data. This often hinges on the use of a good representation where suitable features can simplify tremendously the extraction of knowledge. Traditionally, features were handcrafted based on domain knowledge. However, recent advances in deep learning have shown that it is often better to use a deep structure in which features are automatically learned from data. This has completely revolutionized computer vision, speech recognition and natural language. That being said, feature engineering is now replaced by architecture engineering since practitioners spend enormous time adjusting the architecture and hyperparameters by trial and error. Hence there is a need for techniques to automatically learn the structure and hyperparameters of networks.  In this talk, I will show how to automatically learn the structure of a special class of deep neural networks known as sum-product networks from streaming data.  This will be demonstrated in variety of domains where it is unclear what architecture might work well.(字串長度:1094)