Presented at CS 7643 Deep Learning on September 7, 2017 from 4:30 p.m. – 5:45 p.m. in the Clough Undergraduate Learning Commons (CULC), Room 144, Georgia Tech.Nathan Silberman is the Lead Deep Learning Scientist
at 4Catalyzer where he works on a variety of healthcare related
projects. His machine learning interests include semantic
segmentation, detection and reinforcement learning and how to best
apply these areas to high-impact areas in the medical world. Prior to
joining 4Catalyzer, Nathan was a researcher at Google where among
various projects, he co-wrote TensorFlow-Slim, which is now a
major component of the TensorFlow library. Nathan received his
Ph.D. in 2015 from New York University under Rob Fergus and
David Sontag.CS 7643 Deep LearningRuntime: 68:05 minutesTF-Slim is a TensorFlow-based library with various components. These include modules for
easily defining neural network models with few lines of code, routines for training and evaluating such
models in a highly distributed fashion and utilities for creating efficient data loading pipelines.
Additionally, the TF-Slim Image Models library provides many commonly used networks (ResNet,
Inception, VGG, etc) that make replicating results and creating new networks using existing components
simple and straightforward. I will discuss some of the design choices and constraints that guided our
development process as well as several high-impact projects in the medical domain that utilize most or
all components of the TF-Slim library
