Nvidia Deep Learning Symposium at Duke


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Join us for this Deep Learning Symposium conducted by Nvidia Staff. A boxed lunch will be provided to everyone who pre-registers.

Deep Learning Demystified
Description: Lecture introduces key terminology, use cases from various industries, how deep learning differs from previous algorithmic approach, and how a deep neural network gets trained, optimized, and deployed.

Applied Deep Learning
Description: Lecture covers how to apply deep learning to challenging problems, what types of problems benefit most from deep learning, what skills and knowledge is needed to use deep learning, and the characteristics of successful deep learning projects.

Applications of Deep Learning with Caffe, Theano, and Torch

Frameworks: Caffe, Theano, Torch

Description: This lab introduces the rapidly developing technology of deep learning accelerated by GPUs. The course is intended for anyone looking for a fundamental understanding of deep learning.
In this lab, you will learn:

The concept of deep learning
How the growth of deep learning has improved machine perception tasks including visual perception, speech recognition, and natural language
How to choose which software framework best suits your needs
Image Classification with NVIDIA DIGITS

Frameworks: Caffe

Description: This lab shows you how to leverage deep neural networks (DNN) - specifically convolutional neural networks (CNN) - within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS on top of the Caffe framework and the MNIST hand-written digits dataset.
In this lab, you will learn how to:

Architect a Deep Neural Network to run on a GPU
Manage the process of data preparation, model definition, model training and troubleshooting
Use validation data to test and try different strategies for improving model performance



Status Archived
Date Tuesday, November 7th
Time 11:30am - 4:30pm, 2017
Location Technology Engagement Center Classroom (Telecom Bldg.)
Leader David Williams (Nvidia)
Enrolled 27 of 25