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September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

In-Person Training
NVIDIA Deep Learning Institute bootcamp

Mike Mendelson (NVIDIA)
Sunday, September 17 & Monday, September 18, 9:00am - 5:00pm
Location: Nob Hill 8/9
Secondary topics:  Deep learning
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Early Price ends August 4

This course will sell out—sign up today!

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

NVIDIA Deep Learning Institute-certified instructor Mike Mendelson 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

NVIDIA Deep Learning Institute-certified instructor Mike Mendelson 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–9:30am: Deep learning demystified and applied deep learning

  • 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. Plus, learn 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.

9:30am – 10:30am: Image classification with DIGITS

  • Hands-on exercise: 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.

10:30am–11:00am: Morning break
 
11:00am–12:30pm: Image classification with DIGITS continued

  • Hands-on exercise: continued

12:30pm–1:30pm: Lunch
 
1:30pm–3:00pm: Object detection with DIGITS

  • Hands-on exercise: 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.

3:00pm–3:30pm: Afternoon break
 
3:30 – 5:00 PM: Wrap-up and Q&A


Day 2:

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

  • Hands-on exercise: 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: Modeling time series data with recurrent neural networks in Keras

  • Hands-on exercise: Learn how to create training and testing datasets using electronic health records and prepare datasets for use with RNNs. Plus, construct a long short-term memory model (LSTM) using the Keras library with Theano.

12:30pm–1:30pm: Lunch
 
1:30pm–3:00pm: Neural network deployment with DIGITS and TensorRT

  • Hands-on exercise: 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), the role of batch size in inference performance, and various optimizations that can be made in the inference process and explore inference for a variety of different DNN architectures.

3:00pm–3:30pm: Afternoon break
 
3:30pm–5:00pm: Wrap-up and Q&A

About your instructor

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends August 4.

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Comments

Hengliang Tian | STATISTICAL RESEARCH MODELER
07/20/2017 6:56am PDT

do we need to bring our own laptop? I think NVDIA graphic card is required for this class. Right?