雾计算

Engaging and delighting fans has become the number one priority. However, the identity and behavior of fans within the stadium have been impossible to detect until Sirqul. Furthermore, standalone mobile apps historically have only seen a 5-30% penetration rate, thus leaving venues with poor data, insights and little interaction with the fan itself.This large stadium was looking for a set of recommendations to improve the fan experience, increase revenue and optimize operational efficiency based on this never seen before data.  

 Expedia is committed to continuous innovation, technology, and platform improvements to create a great experience for its customers. The Expedia Worldwide Engineering (EWE) organization supports all websites under the Expedia brand. Expedia began using Amazon Web Services (AWS) in 2010 to launch Expedia Suggest Service (ESS), a typeahead suggestion service that helps customers enter travel, search, and location information correctly. According to the company’s metrics, an error page is the main reason for site abandonment. Expedia wanted global users to find what they were looking for quickly and without errors. At the time, Expedia operated all its services from data centers in Chandler, AZ. The engineering team realized that they had to run ESS in locations physically close to customers to enable a quick and responsive service with minimal network latency.

In this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition.   In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.