Getting Started with Deep Learning

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NVIDIA Deep Learning Institute (DLI) instructor Mr. Craig Tierney will be presenting a 2-hour hands-on lab entitled Getting Started with Deep Learning at TechCode Mountain View. Participants should plan ahead and attend the workshop onsite with your laptop at TechCode Mountain View office. It is a very valuable and technical workshop and you can attend it for free at TechCode AI+ Accelerator!

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

Audience Level: Beginner

Additional information to send to each lab attendee prior to the November 7 workshop:
1. All DLI Lab attendees must create a qwiklLABS account by going to https://nvlabs.qwiklab.com/ prior to getting to the workshop.
2. Test your laptop to ensure qwiklabs runs smoothly 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.

Bio: Craig Tierney is a Solutions Architect in the Worldwide Field Organization at NVIDIA. His primary roles are to support deep learning and high-performance computing. He is focused on applying deep learning to weather and climate research and leveraging the power of typical HPC technologies to accelerate deep learning. Prior to joining NVIDIA, Craig spent more than a decade providing high-performance computing architecture and computational science support to NOAA and several other government and educational organizations including DOE, NASA, DOD and Stanford University. Craig holds a Ph.D. in Aerospace Engineering Sciences from the University of Colorado at Boulder.

Registration link