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Event Details

NVIDIA - A GPU Deep Learning Workshop

9:00 AM - 4:30 PM @ CH2M Hill Alumni Center

Tuesday, April 18, 2017 9:00 AM - 4:30 PM


Description: Oregon State University School of Electrical Engineering and Computer Science, along with NVIDIA are pleased to offer a GPU Deep Learning workshop.

Attend to learn the latest techniques on how to design, train, and deploy neural network-powered machine learning in your applications. You’ll explore widely used open-source frameworks running on NVIDIA’s latest GPU-accelerated deep learning platforms.

Why you should attend: NVIDIA GPUs are the world's fastest and most efficient accelerators delivering world record application performance. This Deep Learning workshop will introduce students to GPU Accelerated Frameworks such as Caffe, Google TensorFlow, Python and Torch using a Hands-on lab approach. After the course, researchers will be able to use Deep Learning to help solve many big data problems such as computer vision, speech recognition, and natural language processing. Practical examples include:

-Vehicle, pedestrian and landmark identification for driver assistance

-Image recognition

-Speech recognition and translation

-Natural language processing

-Life sciences 

Who it's for: Undergraduate/Graduate students, Postdocs, Researchers, Data Scientists and Professors

Hands-On Lab Course Content: 2 Hours - Getting Started with Deep Learning (Caffe, DIGITS)

Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You will walk through the process of data preparation, model definition, model training and troubleshooting. You will use validation data to test and try different strategies for improving model performance using GPUs. On completion of this lab, you will be able to use NVIDIA DIGITS to train a DNN on your own image classification application.

2 Hours - Deep Learning for Image Segmentation (TensorFlow) (uses medical imagery to isolate a particular part of the lung) There are a variety of important applications that need to go beyond detecting individual objects within an image, and that will instead segment the image into spatial regions of interest. An example of image segmentation involves medical imagery analysis, where it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells, so that you can isolate a particular organ. Another example includes self-driving cars, where it is used to understand road scenes. In this lab, you will learn how to train and evaluate an image segmentation network.

2 Hours - Introduction to Recurrent Neural Networks (Python, Torch)  This two-part lab is an introduction to Recurrent Neural Networks (RNN), starting from their foundation. The first part will go through what they are and how they work by learning to train them. The second part will motivate use of RNN for Natural Language Processing using text. RNN can be trained to predict the next character in a sequence of text. Finally, you will see why RNNs have been historically considered hard to train, supplemented by various suggested readings throughout the lab.

Coffee, snacks and drinks will be provided.

About the Instructor: Dr. Jonathan Bentz is a Solution Architect with NVIDIA, focusing on Higher Education and Research customers. In this role he works as a technical resource to customers and OEMs to support and enable adoption of GPU computing. He delivers GPU training such as workshops to train users and help raise awareness of GPU computing. He also works with ISV and customer applications to assist in optimization for GPUs through the use of benchmarking and targeted code development efforts. Prior to NVIDIA, Jonathan worked for Cray as a software engineer where he developed and optimized high performance scientific libraries such as BLAS, LAPACK, and FFT specifically for the Cray platform. Jonathan obtained his PhD in physical chemistry and his MS in computer science from Iowa State University.

Important: Seating is limited to the first 40 people. Please bring your laptop to participate in hands-on exercises. A GPU in your laptop is not required. It is advised that participants review the NVIDIA Getting Started Blog posts and articles beginning with https://developer.nvidia.com/deep-learning

-Deep Learning in a Nutshell Series by Tim Dettmers (University of Lugano, Switzerland)

-Hacker's Guide to Neural Networks by Andrej Karpathy (Stanford University)

-Getting Started with DIGITS by Allison Gray (NVIDIA)

-Deep Learning Posts on the ParallelForAll technical blog

Please also take the time to register at our CUDA developer site:


CH2M HILL Alumni Center (campus map)
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