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Presentations:

Introduction to Machine & Deep Learning

Frontiers for Deep Learning and Cancer - Eric Stahlberg, Frederick National Laboratory for Cancer Research:

Introduction to Deep Learning - Rick Stevens, Argonne National Laboratory:

Deep Learning: Perspectives from the NIH

Accelerating Image Analysis Workflows Using Deep Learning - Yanling Liu, Frederick National Laboratory for Cancer Research:

The Impact of Deep Learning on Radiology - Ronald Summers, Diagnostic Radiology Department, NIH Clinical Center: 

Applying Deep Learning to Big Data: Perspectives from the Department of Energy with Applications in Health

Cellular Level Deep Learning - Fangfang Xia, Argonne National Laboratory:

Molecular Level Deep Learning - Brian Van Essen, Lawrence Livermore National Laboratory: 

Population Level Deep Learning - Arvind Ramanathan, Oak Ridge National Laboratory: 

Getting Started With CANDLE - Rick Stevens, Argonne National Laboratory:

Overview of CANDLE Layers: Workflows, Scripting, and Parallelization Strategies

Workflows - Justin Wozniak, Argonne National Laboratory:

Scripting - Tom Brettin, Argonne National Laboratory:

Parallelization Strategies - Brian Van Essen, Lawrence Livermore National Laboratory:

Preparing Data for Deep Learning

Arvind Ramanathan, Oak Ridge National Laboratory:

Fangfang Xia, Argonne National Laboratory:

Brian Van Essen, Lawrence Livermore National Laboratory:

Interactive Session with CANDLE Developers

Hyperparameter Optimization and Uncertainty Quantification - Justin Wozniak, Argonne National Laboratory:

Installing CANDLE into Other Environments - George Zaki, Frederick National Laboratory for Cancer Research:

Workshop Recordings:

Day 1 - Wednesday, February 21, 2018: 

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