Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

In-Person Training
NVIDIA Deep Learning Institute bootcamp

Charles Killam (NVIDIA)
Monday, June 26 & Tuesday, June 27, 9:00am - 5:00pm
Location: Bryant
Secondary topics:  Deep Learning
See pricing & packages
Early Price ends May 12

This course will sell out—sign up today!

Participants should plan to attend both days of this 2-day training course. Training passes do not include access to tutorials on Tuesday.

In this two-day workshop blending lecture and hands-on, real-world exercises, NVIDIA Deep Learning Institute-certified instructor Charlie Killam walks you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to talking on real-word problems using deep learning.

What you'll learn, and how you can apply it

  • Understand deep learning basic concepts and terminology
  • Learn how to leverage deep neural networks to solve real-world image classification problems, how to detect objected using trained neural networks, and how to train and evaluate an image segmentation network

In this two-day workshop blending lecture and hands-on, real-world exercises, NVIDIA Deep Learning Institute-certified instructor Charlie Killam walks you through solving the most challenging problems with deep learning. You’ll start with deep learning basic concepts and quickly move to talking on real-word problems using deep learning.

Outline

Day 1

9:00am–10:30am: Deep learning demystified

  • Lecture: General background on deep learning, key terminology, use cases from various industries, how deep learning differs from previous algorithmic approach, and how a deep neural network gets trained, optimized, and deployed

10:30am–11:00am: Morning break
 
11:00am–12:30pm: Applied deep learning

  • Lecture: How to apply deep learning to challenging problems, what types of problems benefit most from deep learning, what skills and knowledge is needed to use deep learning, and the characteristics of successful deep learning projects

12:30pm–1:30pm: Lunch
 
1:30pm–3:00pm: Getting started with deep learning

  • Hands-on lab: Leverage deep neural networks (DNN) within the deep learning workflow, solve a real-world image classification problem using NVIDIA DIGITS, walk through the process of data preparation, model definition, model training, and troubleshooting, use validation data to test and try different strategies for improving model performance using GPUs, and use NVIDIA DIGITS to train a DNN on your own image classification application

3:00pm–3:30pm: Afternoon break
 
3:30pm–5:00pm: Approaches to object detection

  • Hands-on lab: Learn three approaches to identify a specific feature within an image, compare each in relation to model training time, model accuracy, and speed of detection during deployment, understand the merits of each approach, and learn how to detect objects using trained neural networks


Day 2

9:00am–10:30am: Image Segmentation with TensorFlow

  • Hands-on lab: Deep learning for image segmentation—Learn how to train and evaluate an image segmentation network

10:30am–11:00am: Morning break

11:00am–12:30pm: Neural network deployment

  • Hands-on lab: Learn three approaches for deployment (directly use inference functionality within a deep learning framework, integrate inference within a custom application, and use the NVIDIA TensorRT), learn about the role of batch size in inference performance, learn about various optimizations that can be made in the inference process, and explore inference for a variety of different DNN architectures

12:30pm–1:30pm: Lunch
 
1:30pm–3:00pm: Course wrap-up, Q&A, and individual help

3:00pm–3:30pm: Afternoon break
 
3:30pm–5:00pm: Course wrap-up, Q&A, and individual help (continued)

About your instructor

Charles Killam is a certified instructor and curriculum designer at NVIDIA’s Deep Learning Institute. Though Charlie works across all verticals, his efforts focus primarily on the application of deep neural networks (DNNs) in the healthcare space. Over his career, Charlie has delivered a data analytics bootcamp for Northeastern University, a geospatial Tableau project for Stanford University, and, working with MADlib, an open source machine-learning algorithm library at Pivotal.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends May 12.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)