CVPR18: Tutorial: Part 1: Building Deep Learning Applications on Big Data Platforms
Organizers: Jason (Jinquan) Dai Location: Room 151 A-C & G Time: 0900-1200 (Half Day — Morning) Description: Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows. Schedule: 0900 Motivation 0910 Overview 0930 Analytics Zoo for Spark and BigDL 1000 Morning Break 1030 Distributed Training and Inference 1100 Advanced Applications 1130 Real-World Applications 1150 Q&A
Organizers: Jason (Jinquan) Dai Location: Room 151 A-C & G Time: 0900-1200 (Half Day — Morning) Description: Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows. Schedule: 0900 Motivation 0910 Overview 0930 Analytics Zoo for Spark and BigDL 1000 Morning Break 1030 Distributed Training and Inference 1100 Advanced Applications 1130 Real-World Applications 1150 Q&A
