Bringing deep learning into big data analytics using BigDL
Xianyan Jia and Zhenhua Wang explore deep learning applications built successfully with BigDL. They also teach you how to develop fast prototypes with BigDL's off-the-shelf deep learning toolkit and build end-to-end deep learning applications with flexibility and scalability using BigDL on Spark.
| Talk Title | Bringing deep learning into big data analytics using BigDL |
| Speakers | Xianyan Jia (Intel), zhenhua wang (JD.com) |
| Conference | Strata + Hadoop World |
| Conf Tag | Make Data Work |
| Location | Singapore |
| Date | December 6-8, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Deep learning is rapidly becoming one of the most successful and widely applicable sets of techniques in use today. But while cutting-edge deep learning research is emerging with breathtaking speed, there is often a gap between papers and prototypes—and an even larger gap between prototypes and production. BigDL provides scalable deep learning functionalities on Apache Spark and native support for Spark ML pipelines for scalable deep learning training and inference. Xianyan Jia and Zhenhua Wang explore deep learning applications built successfully with BigDL, including neural recommendations, fraud detection, object detection with SSD, speech recognition (DS2), and 3D medical imaging analysis. You’ll learn how to develop fast prototypes with BigDL’s off-the-shelf deep learning toolkit and build end-to-end deep learning applications with flexibility and scalability using BigDL on Spark.
