Getting Started with GPU Accelerated Deep Learning
Submitted by Gaurav Goswami (@gauravgoswami) on Monday, 10 July 2017
Deep learning has been applied to various domains with great success and is a popular technique to solve challenging machine learning problems in the real world. However, deep learning is also computationally expensive and it is not feasible to train a deep network in a reasonable time frame on large databases without using GPU acceleration. In this talk, I will provide a tutorial on how to setup the pre-requisite drivers, packages, and tools to get started with GPU enabled deep learning on a machine running Ubuntu OS. In addition, we will take a quick look at running a deep learning workload on the GPU via a Jupyter notebook and conclude with an overview of the performance difference when using a CPU vs GPU for deep learning.
Draft slides are available here: https://www.slideshare.net/secret/3SNabBqd0O7HQU. I will update these to the full version soon. I would ideally like to showcase aspects of the workflow including the workload notebook live in action during the talk.
I am currently working at IBM India as an Artificial Intelligence/Machine Learning expert. My Ph.D. thesis focuses on using machine learning and computer vision techniques to solve challenges in face recognition by improving the computation and combination of robust representations. I have had the opportunity to be a part of crafting a machine learning based solution for real world use cases in these domains.