Workshop:
Deep Learning with NVIDIA
Deep learning with NVIDIA
Format: Lectures and hands-on exercises.
This workshop will cover materials from two courses from the NVIDIA Deep Learning Institute, in accelerated computing using GPUs, and in deep learning.
Workshop attendee instructions
- Create a qwikLABS account prior to getting to the conference.
- Ensure qwikLABS runs smoothly on your laptop by going to http://websocketstest.com/.
- Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
- If there are issues with WebSockets, try updating your browser. Best browsers for qwikLABS are Chrome, FireFox and Safari. The labs will run in IE but it is not an optimal experience.
- You must bring your own laptop to this workshop.
Introduction to Accelerated Computing
Learn about the three techniques for accelerating code on a GPU; Libraries, Directives like OpenACC, and writing code directly in CUDA-enabled languages. In 45 minutes, you will work through a few different exercises demonstrating the potential speed-ups and ease of use of porting to the GPU. Duration: 45minutes. Audience level: Beginner
Getting Started with Deep Learning
Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data. This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You’ll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset. Prerequisites: Basic knowledge of data science and machine learning. Duration: 2 hours. Audience Level: Beginner