Washington D.C. NOV 1-2, 2017

2016 Deep Learning Training Labs

Train directly through NVIDIA Deep Learning Institute instructor-led labs at GTC DC. Learn how advanced deep learning techniques are being applied to rich data sets in order to help solve big problems. Upon completion of an NVIDIA Deep Learning Institute training lab, you will receive a certificate of attendance and free online training credits.

Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data. This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a challenging real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You’ll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset.

Prerequisites: Basic knowledge of data science and machine learning
Audience Level: Beginner
Building upon the foundational understanding of how deep learning is applied to image classification, this lab explores different approaches to the more challenging problem of detecting if an object of interest is present within an image and recognizing its precise location within the image. Numerous approaches have been proposed for training deep neural networks for this task, each having pros and cons in relation to model training time, model accuracy and speed of detection during deployment. On completion of this lab you will understand each approach and their relative merits. You’ll receive hands-on training applying cutting edge object detection networks using NVIDIA DIGITS on a challenging real-world dataset.

Prerequisites: Basic knowledge of data science and machine learning
Audience Level: Beginner
Deep learning software frameworks leverage GPU acceleration to train deep neural networks (DNNs). But what do you do with a DNN once you have trained it? The process of applying a trained DNN to new test data is often referred to as ‘inference’ or ‘deployment’. In this lab you will test three different approaches to deploying a trained DNN for inference. The first approach is to directly use inference functionality within a deep learning framework, in this case DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe but this time through its Python API. The final approach is to use the NVIDIA TensorRT™ which will automatically create an optimized inference run-time from a trained Caffe model and network description file. You will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process. You’ll also explore inference for a variety of different DNN architectures trained in other DLI labs.

Prerequisites: C++ programming experience
Audience Level: Intermediate
There are a variety of important applications that need to go beyond detecting individual objects within an image and instead segment the image into spatial regions of interest. For example, in medical imagery analysis it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells so that we can isolate a particular organ. In this lab we will use the TensorFlow deep learning framework to train and evaluate an image segmentation network using a medical imagery dataset.

Prerequisites: Basic knowledge of TensorFlow
Audience Level: Intermediate

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Deep Learning Institute

See what the Deep Learning Institute program is all about. Learn how advanced deep learning techniques are being applied to these rich datasets to help solve problems.

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Deep Learning Self-Paced Course

Learn everything you need to design, train, and integrate neural network powered machine learning into our applications with widely used open-source frameworks and the NVIDIA deep learning platform.

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