Come join us as we dive into how we're applying deep learning across Twitter and solving some of the challenges our engineers face. In order to attend you must RSVP on the registration link below. AGENDA: 6pm Doors Open 6:30pm Tech Talks Begin 7:15pm Q&A 7:30 - 8pm Networking TOPICS INCLUDE: CHALLENGES IN RECOMMENDER SYSTEMS - ASHISH BANSAL "Twitter has amazing and unique content that is generated at an enormous velocity internationally. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Approaches span collaborative filtering and content based recommendation systems for different use cases. This talk gives insight into unique recommendation system challenges at Twitter's scale and what makes this a fun and challenging task."

BAIDU results indicate that in many real world contexts, simply scaling your training data set and models is likely to predictably improve the model's accuracy. This predictable behavior may help practitioners and researchers approach debugging and target better accuracy scaling. On the extreme other end, @BaiduResearch's thorough analysis on scaling properties of neural networks would cost around $2 million USD on AWS Glad they did it and are exporting their knowledge _ pic.twitter.com/0OUYfpWXrK Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products.

Deep neural networks--a form of artificial intelligence--have demonstrated mastery of tasks once thought uniquely human. Their triumphs have ranged from identifying animals in images, to recognizing human speech, to winning complex strategy games, among other successes. Now, researchers are eager to apply this computational technique--commonly referred to as deep learning--to some of science's most persistent mysteries. But because scientific data often looks much different from the data used for animal photos and speech, developing the right artificial neural network can feel like an impossible guessing game for nonexperts. To expand the benefits of deep learning for science, researchers need new tools to build high-performing neural networks that don't require specialized knowledge.