February 22, 2019

9:00 am

Fundamentals of deep learning for computer vision hands on lab

12:00 pm

Lunch

1:00 pm

Deep learning for computer vision lab continued

3:00 pm

Break

3:15 pm

Introduction to RAPIDS with demonstrations

4:30 pm

Social and networking

Workshop Location

Instructors

Abe Stern

Abe Stern is a solutions architect at NVIDIA focusing on higher education and research. His current projects include the investigation of using deep generative models for molecular discovery and scaling AI up to harness HPC-sized resources. Abe's formal background is in computational chemistry. Prior to joining NVIDIA, Abe was a post doctoral at University of California, Irvine and completed his Ph.D. at the University of South Florida. Fun fact: Abe spent one summer semester at the University of Washington's Friday Harbor Laboratories in the San Juan Islands studying functional morphology and shark swimming biomechanics.

Fundamentals of Deep Learning for Computer Vision workshop teaches you to apply deep learning techniques to a range of computer vision tasks through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows to train and deploy neural network models on a fully-configured, GPU accelerated workstation in the cloud.

After a quick introduction to deep learning, you will advance to building and deploying deep learning applications for image classification and object detection, followed by modifying your neural networks to improve their accuracy and performance, and finish by implementing the workflow that you have learned on a final project. At the end of the workshop, you will have access to additional resources to create new deep learning applications on your own.

At the conclusion of the workshop, you will have an understanding of the fundamentals of deep learning and be able to:

• Implement common deep learning workflows, such as image classification and object detection.
• Experiment with data, training parameters, network structure, and other strategies to increase performance and capability of neural networks.
• Integrate and deploy neural networks in your own applications to start solving sophisticated real-world problems.

Every students needs a laptop computer and runs a browser to the cloud. Best browsers for the labs are Chrome, Firefox and Safari. The labs will run in IE but it is not an optimal experience. This is an introductory class, and no experience with deep neural networks is required.

Note Please bring your fully charged laptop to participate, a GPU in your laptop is not required.

Sponsors