IMPORTANT: Please follow these pre-workshop instructions.

Attendee Set Up Requirements
To maximize your training time during your DLI training, please follow the instructions below, before attending your first training session:

1. You must bring your own laptop in order to run the training. Please bring your laptop and its charger.
2. A current browser is needed. For optimal performance, Chrome, Firefox, or Safari for Macs are recommended. IE is operational but does not provide the best performance.
3. Create an account at http://courses.nvidia.com/join. Click the “Create account” link to create a new account. If you are told your account already exists, please try logging in instead. If you are asked to link your “NVIDIA Account” with your “Developer Account”, just follow the on-screen directions.
4. Ensure your laptop will run smoothly by going to http://websocketstest.com/. Make sure that WebSockets work by ensuring Websockets is supported under “Environment”. Additionally, make sure that “Data Receive”, “Data Send” and “Echo Test” all check Yes under “WebSockets”. If there are issues with WebSockets, try updating your browser.

If you have any questions, please contact [email protected].

Learn how to apply convolutional neural networks (CNNs) to detect chromosome co-deletion and search for motifs in genomic sequences.

This course teaches you how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences. You’ll learn how to:

• Understand the basics of convolutional neural networks (CNNs) and how they work
• Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status
• Use the DragoNN toolkit to simulate genomic data and to search for motifs

• 08:30 Registration
• 09:00 Welcome
• 09:15 Image Classification with Digits
• 10.30 Morning Break
• 11.00 Image Classification with Digits (Continued)
• 11.45 Deep Learning for Genomics using DragoNN with Keras and Theano
• 12:30 Lunch
• 13:30 Deep Learning for Genomics using DragoNN with Keras and Theano (Continued)
• 14:45 Radiomics 1p19q Chromosome Image Classification with TensorFlow
• 15:30 Afternoon Break
• 16:00 Radiomics 1p19q Chromosome Image Classification with TensorFlow (Continued)
• 17:15 Closing Comments and Questions

LAB #1: IMAGE CLASSIFICATION WITH DIGITS (120MINS)

Learn to interpret deep learning models to discover predictive genome sequence patterns using the DragoNN toolkit on simulated and real regulatory genomic data.

LAB #2: DEEP LEARNING FOR GENOMICS USING DRAGONN WITH KERAS AND THEANO (120 MINS)

Learn to interpret deep learning models to discover predictive genome sequence patterns using the DragoNN toolkit on simulated and real regulatory genomic data.

LAB #3: RADIOMICS 1P19Q CHROMOSOME IMAGE CLASSIFICATION WITH TENSORFLOW (120 MINS)

Learn how to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.

PRE-REQUISITES

• Basic familiarity with deep neural networks, and basic coding experience in Python or a similar language.