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2018 GTC Washington DC
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Browse all available sessions and instructor-led trainings below. Click “Add to Schedule” to build your personal agenda and reserve your place at instructor-led trainings.

Please note:

  • DLI instructor-led workshops must be purchased separately through registration. See GTC Pricing for more information.
  • To attend instructor-led trainings, you must have a Conference & Training pass. You must also reserve your place at each instructor-led training you wish to attend. If you have reserved a place at an instructor-led training, please arrive on time to ensure you do not lose your seat.
  • Attendance to sessions is first come, first served. Please arrive early to the sessions you wish to attend to guarantee entry.



DLIW01 - Fundamentals of Deep Learning for Computer Vision

Prerequisites: Basic technical background

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this hands-on course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

  • Implement common deep learning workflows, such as image classification and object detection.
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
  • Deploy your neural networks to start solving real-world problems.

Upon completion of this workshop, you’ll be able to start solving problems on your own with deep learning.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop Certified DLI Instructor
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DLIW02 - Fundamentals of Accelerated Computing with CUDA C/C++

Prerequisites: Basic C/C++ competency

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Experience C/C++ application acceleration by:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop
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DLIW03 - Deep Learning for Healthcare Image Analysis

Prerequisites: Basic familiarity with concepts of deep learning and convolutional neural networks

This hands-on course explores how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You’ll learn how to:

  • Perform image segmentation on MRI images to determine the location of the left ventricle.
  • Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease.
  • Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status.

Upon completion of this workshop, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop
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DLIW04 - Fundamentals of Deep Learning for Natural Language Processing

Prerequisites: Basic experience with neural networks

Explore the latest techniques for understanding textual input using natural language processing (NLP). You’ll learn how to:

  • Convert text to machine understandable representation and classical approaches
  • Implement distributed representations (embeddings) and understand their properties
  • Train Machine Translators from one language to another

Upon completion of this workshop, you’ll be proficient in NLP using embeddings in similar applications.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

DLI Instructor-Led Workshop
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DC8104 - GPU Accelerated Data Science In this session, we will explore the latest work, showcase benchmarks, and provide demos of the GPU Open Analytics Initiative (GoAi), a collection of open-source libraries, frameworks, and APIs established to standardize GPU analytics to allow for easier development and enhanced performance for GPU-accelerated analytics technologies. Numerous Fortune 500 customers experience latency and performance issues in their data pipeline. Big data frameworks and solutions tried to address this problem, but the cost to scale to the volume and velocity of current needs has proven to be prohibitively expensive. GoAi is addressing these challenges with a vision is to create an end-to-end GPU-accelerated data pipeline that will smooth onboarding ramp for enterprises to explore and integrate AI into their core data driven decision making processes. The session will also provide examples of how customers are benefiting from early primitives and outperforming CPU equivalents. Talk Joshua Patterson - Director of Applied Solutions Engineering, NVIDIA
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DC8108 - Deep Learning Demystified What is Deep Learning? In what fields is it useful, and how does it relate to artificial intelligence? During this session, we'll get an understanding of deep learning and why this powerful new technology is getting so much attention. Learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results. Talk Will Ramey - Director, Developer Programs, NVIDIA
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DC8149 - AI in the Driver's Seat We will present a survey of the latest AI technology for autonomous vehicle development from training to simulation to testing. Talk Danny Shapiro - Senior Director, Automotive, NVIDIA
Derek Kan - Under Secretary for Policy, Department of Transportation
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DC8156 - Accelerate Video Analytics Development with DeepStream 2.0 This talk explores how DeepStream enables developers to create high-stream density applications with deep learning and accelerated multimedia image processing, building IVA solutions at scale. Leverage a heterogeneous concurrent neural network architecture to bring in different deep learning techniques for more intelligent insights. The framework makes it easy to create flexible and intuitive graph-based applications, resulting in highly optimized pipelines for maximum throughput. Talk Saurabh Jain - Business Development, NVIDIA
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DC8175 - Accelerating Understanding: The Convergence of HPC and AI in a Post-Moore Law's World Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/Deep Learning. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emergent workflow that uses traditional HPC numeric simulations to generate the labeled data sets required to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore's Law world considered. Talk Steve Oberlin - Chief Technology Officer, Accelerated Computing, NVIDIA
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DC8185 - Artificial Intelligence: How Do We Get It to the Clinic? TBA Panel Carla Leibowitz - Head of Strategy and Marketing, Arterys
Abdul Hamid Halabi - Global Business Development Lead, Healthcare & Life Sciences, NVIDIA
Elizabeth Jones - Acting Director, Radiology and Imaging Sciences, National Institutes of Health (NIH)
Bakul Patel - Associate Director for Digital Health, Food and Drug Adminsitration (FDA)
Agata Anthony - Regulatory Affairs Executive, GE Healthcare
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DC8188 - American Leadership Through AI Research The U.S. is the world leader in developing AI technologies, but other countries are catching up. What must the U.S. do to sustain and strengthen its global leadership in AI research and development? What are the challenges and what more can and should be done by industry and the government to advance AI? Panel Michael Kratsios - Deputy Assistant to the President for Technology Policy, The White House Office of Science and Technology Policy
James Kurose - Assistant Director, National Science Foundation (NSF)
Keoki Jackson - CTO, Lockheed Martin
David Luebke - Vice President, Graphics Research, NVIDIA
Howie Choset - Professor of Robotics, Carnegie Mellon University
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DLIS05 - Applying Natural Language Processing on News Data using Deep Learning

Prerequisites: Advanced experience with neural networks and knowledge of financial industry

Learn the fundamentals of natural language processing (NLP) as it applies to the generation of trade signals from real-time news data. In this session, you'll leverage a dataset of news article headlines to:

  • Train neural networks to predict market direction
  • Understand several popular models for text representation including bag-of-words, Word2Vec, GloVe, and Doc2Vec
  • Appreciate the effectiveness of LSTM, CNN, and feed-forward deep neural networks
  • Explore techniques used for improving training times with NVIDIA GPUs and the cuDNN library

Upon completion, you'll be able to apply NLP to generate trade signals from real-time news data. 

Instructor-Led Training DLI certified instructor
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DLIS09 - Develop Scalable IVA Applications using DeepStream

Prerequisites: Experience with C++

Understanding video requires multi-stream decoding/encoding, scaling, color space conversion, tracking, and multi-stage inference. DeepStream SDK allows developers to focus on core deep learning development, while offering the best system level software optimization and performance. You'll learn how to:

  • Build modular and scalable multi-stream applications using heterogenous concurrent neural networks for video analytics
  • Utilize hardware accelerated multi-media processing and optimized memory management for high stream density and throughput
  • Utilize included sample code and pre-trained models for image classification, understanding, categorization and filtering

Upon completion, you'll know how to create AI-based video analytics applications using DeepStream to transform video into valuable insights.

Instructor-Led Training DLI certified instructor
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DLIS10 - Introduction to Object Detection with TensorFlow

Prerequisites: None

Get started with an introduction to object detection and image segmentation. You'll explore the shift from traditional computer vision techniques to innovative methods based on deep learning and convolution neural networks (CNNs) for object detection. You'll learn how to:

  • Leverage CNNs to iteratively improve performance and expand image understanding capabilities
  • Use Microsoft Common Object in Contexts dataset and the Google object detection API in TensorFlow
  • Determine accuracy vs. performance trade-offs between Single Shot Multibox Detectors (SSDs), Faster R-CNN with residual networks, and Mask R-CNN

Upon completion, you'll understand how to implement object detection networks with the API in TensorFlow.

Instructor-Led Training DLI certified instructor
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DC8128 - Deep Learning for Smart Home Monitoring We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home monitoring. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes. Talk Hongcheng Wang - Senior Manager, Technical R&D, Comcast Applied AI Research
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DC8171 - Training Mixed Precision Neural Networks with Tensor Cores: Theory and Practice Tensor Cores, introduced with the Volta GPU architecture, provide up to 125 teraflops of throughput for operations on IEEE half-precision floats. In the theory portion of this talk we will review the half-precision format, the features of Tensor Cores, and principles for building mixed precision neural networks in any framework. The practice portion will review these principles with examples in PyTorch and show how tools like Apex can automatically convert existing neural networks to use Tensor Cores. This conversion requires no change in model architecture or hyperparameters and has been successfully applied to visual, auditory, and linguistic tasks on multiple frameworks. Talk Christian Sarofeen - Senior Dev Tech Engineer, NVIDIA
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DC8182 - Delivering Safety and Efficiency via Automation in Trucks and Buses This presentation will provide an overview of how automation can help deliver value to bus and truck fleet customers. With the convergence of electrification, connectivity and automation, there is a tremendous push to unlock the value of the technologies to the customer. Attendees will be introduced to the challenges and opportunities in the commercial vehicle industry. Talk Brendan Chan - Engineering Group Lead - ADAS Technology, Navistar, Inc.
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DC8186 - Better Serving Americans with AI AI's potential cuts across all industries, from agriculture to healthcare to oil and gas and more. But it also can help government agencies be more efficient, better at identifying waste and fraud, and more responsive and convenient for all Americans. This panel will discuss the various AI applications that can make government smarter, and how we get there. Panel Josh Sullivan - Senior Vice President, Booz Allen Hamilton
Cameron Chehreh - CTO/Vice President, Pre-Sales Engineering, Dell EMC Federal
Austin Carson - Government Relations, NVIDIA
Michael Garris - Founder/Chair, Artificial Intelligence(AI) Community of Interest, National Institute of Standards and Technology (NIST)
Dominic Delmolino - Managing Director, Chief Technology Officer, Accenture Federal Services
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DC8194 - The Expanding Role of Computation at the Frontiers of Science High fidelity, multiscale, multiphysics simulations have firmly established computing and, in particular, HPC, as a research pillar which, with experiment and theory, forms the foundation of the discovery process in many fields of science and engineering. Scientific Big Data such as that produced from diverse and distributed sensor networks, and new instruments, offers not only unprecedented opportunities for new understanding from more advanced simulations but the ability to explore fundamentally new understanding based on emerging computational approaches based in machine learning and artificial intelligence. This talk will discuss opportunities, recent developments, and challenges as the research communities begin to embrace these methodologies. Talk Irene Qualters - Senior Science Advisor, National Science Foundation
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DC8195 - Deep Learning for the Early Detection of Pancreatic Cancer on CT Scanning The talk will focus on our 2 year journey to develop deep learning algorithms for the detection of pancreatic cancer on CT scans. The effort of a multi-disciplinary teams of Computer Scientists, Radiologists, Oncologists, and Pathologists determined that deep learning could potentially change the trajectory of pancreatic cancer survival (currently under 7% at 5 years) by early detection and avoiding what has been documented as an up to 25% false negative rate. The session will address the challenges of creating over 1100 normal studies to train the computer and then over 1500 pancreatic cancer studies to train the algorithms in booth organ segmentation and then tumor detection and analysis We will discuss what successes we have had in lesion detection and what changes remain . We will also discuss the role of Radiomics with deep learning as a way of potentially improving lesion detection and eventually lesion classification , The talk will illustrate the work done with a series of cases studies showing both the successes and challenges of this work. Finally we will address we we see this work going and the opportunities for introducing this into clinical practice. Talk Elliot Fishman - Professor of Radiology, Oncology, Surgery and Urology, Johns Hopkins Hospital
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DC8138 - Training and Inferencing DL Models with NVIDIA GPU and MapR Kubernetes Volume Plugin The audience will learn examples and common practices for using Kubernetes to leverage NVIDIA GPU computing power when building DL models. We use a converged data platform to serve as data infrastructure, providing distributed file system and key-value storage and streams. Kubernetes is an orchestration layer that manages containers to scale out the training and deployment of DL models using heterogeneous GPU clusters. We also leverage the ability to publish and subscribe to streams on the platform to build next-gen applications with DL models, and monitor the model performance and shift of feature distributions. Talk Jim Scott - Director, Enterprise Strategy and Architecture, MapR
Jim Scott - Director, Enterprise Strategy and Architecture, MapR
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DC8152 - Accelerated-Node Software Technologies and Applications in the U.S. Department of Energy Exascale Computing Project The vision of the Exascale Computing Project, initiated in 2016 as a formal U.S. Department of Energy project executing through 2022, is to accelerate innovation with exascale simulation and data science solutions. After a brief overview of this, we will give illustrative examples on how the ECP teams are leveraging, exploiting, and advancing accelerated-node software technologies and applications on hardware such as the powerful GPUs provided by NVIDIA. We will summarize best practices and lessons learned from these accelerated-node experiences along with ECP's plans moving into the exascale era, which is on the now near-term horizon. These solutions will enhance U.S. economic competitiveness, change our quality of life, and strengthen our national security. ECP's mission is to deliver exascale-ready applications and solutions that address currently intractable problems of strategic importance and national interest; create and deploy an expanded and vertically integrated software stack on DOE HPC exascale and pre-exascale systems, defining the enduring US exascale ecosystem; and leverage U.S. HPC vendor research activities and products into DOE HPC exascale systems. The project is a joint effort of two DOE programs: the Office of Science Advanced Scientific Computing Research Program and the National Nuclear Security Administration Advanced Simulation and Computing Program. ECP's RD&D activities, which encompass the development of applications, software technologies, and hardware technologies and architectures, is carried out by over 100 small teams of scientists and engineers from the DOE national laboratories, universities, and industries. Talk Doug Kothe - Director, Exascale Computing Project, Oak Ridge National Lab
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DC8165 - Safety by Way of Supercompute The computational complexity of solving for vehicle autonomy is dramatically increased by safety requirements. We will present key tenants of solving for safety via supercompute, including redundancy, diversity, and AI. Talk Neda Cvijetic - Senior Manager, Autonomous Vehicles, NVIDIA
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DC8173 - AI Powered Distributed Computer Vision Enabling Smart City IoT Platforms Cities are always looking for new ways to maintain high standards of living, better connect with citizens and find ways to save money—all while serving growing populations. As city population densities increase and cities strive to increase walkability and mobility for their citizens, they have a big focus on a holistic approach to traffic safety. As part of their efforts to become smarter, more and more cities are turning to the Internet of Things (IoT) and Machine-to- Machine (M2M) technologies to improve municipal services, create additional sources of revenue, and enable city management in new and creative ways. Talk Csaba Rekeczky - Director, Computer Vision and Predictive Analytics, Verizon
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DC8174 - Turbocharging the AI Pipeline with Python and Anaconda The rise of GPU-accelerated data science and AI has come about through a combination of open source innovation and better tooling to support reproducible workflows. However, as the diverse array of deep learning libraries continue to mature, attention is moving to other parts of the AI pipeline, including simulation, ETL, and deployment. In this talk, I'll review open source projects that address these other areas, such as Numba, for implementing custom simulations and data transformations on the GPU, and PyGDF, for GPU accelerated dataframes. I'll discuss how the Anaconda Distribution and its conda packaging system helps data scientists create reproducible environments and deploy models. Finally, I'll talk about how Anaconda Enterprise allows data science teams to collaborate efficiently on GPU-accelerated projects with each other, and supports AI workflows from data exploration all the way to deployment. Talk Stanley Seibert - Director of Community Innovation, Anaconda
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DC8187 - Transforming Agriculture with AI Artificial intelligence has opened a new class of efficient technologies to help the American farmer from using drones to monitoring their crops to using task tracking systems that evaluate watering and seeding. This panel will discuss the latest AI innovations for the farm and the policies that will continue to advance U.S. agriculture through the 21st Century. Panel Dionne Toombs - Director for the Office of the Chief Scientist, USDA
George Kantor - Senior Systems Scientist, Robotics Institute, Carnegie Mellon University
Jesse Clayton - Senior Manager, Product Management for Intelligent Machines, NVIDIA
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DLIS04 - Detection of Anomalies in Financial Transactions using Deep Autoencoder Networks

Prerequisites: Advanced experience with neural networks and knowledge of financial industry

The "unsupervised" and "end-to-end" detection of anomalies in transactional data is one of the long-standing challenges in financial statement audits or fraud investigations. In this session, you'll explore how autoencoder neural network can be trained to detect anomalies by learning a compressed but "lossy" model of regular transactions. You'll learn:

  • Basic concepts, intuition and major building blocks of autoencoder neural networks · How to pre-process financial data in order to learn a model of its characteristics
  • How to design, implement, and train a deep autoencoder network using PyTorch to detect anomalies in large-scale financial data
  • How to interpret and evaluate the network's detection results as well as its reconstruction loss

Upon completion, you'll understand how to train deep autoencoder neural networks to detect anomalies in financial transactions.

Instructor-Led Training DLI certified instructor
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DLIS08 - Medical Image Classification Using the MedNIST Dataset

Prerequisites: None

Get a hands-on practical introduction to deep learning for radiology and medical imaging. You'll learn how to:

  • Collect, format, and standardize medical image data
  • Architect and train a convolutional neural network (CNN) on a dataset
  • Use the trained model to classify new medical images

Upon completion, you’ll be able to apply CNNs to classify images in a medical imaging dataset.

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DLSI12 - Introduction to CUDA Python with Numba

Prerequisites: Basic Python and Numpy Competency

Explore an introduction to Numba, a just-in-time function compiler that allows developers to utilize the CUDA platform in their Python applications. You'll learn how to:

  • Decorate Python functions to be compiled by Numba
  • Use Numba to GPU accelerate NumPy ufuncs


Upon completion, you'll be able to use Numba to GPU-accelerate NumPy ufuncs in your Python code, and will be ready to learn how to write custom CUDA kernels in Python.

Instructor-Led Training DLI certified instructor
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DC8144 - The Keys to Deploying Self-Driving Cars Autonomous vehicles can drastically increase the safety of U.S. roads, where federal officials estimate 94% of all traffic accidents are caused by human error. In addition to road safety, AVs can also improve the quality of life, reducing congestion and freeing up valuable time spent in the car. To realize these significant benefits, manufacturers must develop this technology safely and comprehensively. This session will explore how breakthrough technologies like virtual reality simulation and deep learning are helping to accelerate safe development and deployment of AVs. It will also address, how are manufacturers and regulators can work to ensure the rules of the road evolve with autonomous driving technology, and the further benefits that driverless vehicles bring to our everyday lives. Panel Finch Fulton - Deputy Assistant Secretary for Transportation Policy, U.S. Department of Transportation
Brad Stertz - Director, Audi Government Affairs, Audi
Danny Shapiro - Senior Director, Automotive, NVIDIA
Bert Kaufman - Head, Corporate and Regulatory Affairs, Zoox
Danny Shapiro - Senior Director, Automotive, NVIDIA
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DC8153 - Large-scale AI deployments with Kubernetes on NVIDIA GPUs In this session, we'll explore some of the common challenges with scaling-out deep learning training and inference deployment on data centers and public cloud using Kubernetes on NVIDIA GPUs. Through examples, we'll review a typical workflow for AI deployments on Kubernetes. We'll discuss advanced deployment options such as deploying to heterogenous GPU clusters, specifying GPU memory requirements, and analyzing and monitoring GPU utilizations using NVIDIA DCGM, Prometheus and Grafana. Talk Shashank Prasanna - Product Marketing Manager, NVIDIA
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DC8213 - Scaling Video Intelligence for Responsive Solutions at the Edge From managing wilderness to urban environments, deploying & integrating visual intelligence is critical but challenging. We will discuss a number of scenarios where deploying deep learning right at the edge solves scalability, networking, and responsiveness constraints. Boulder AI will showcase how deep learning and NVIDIA Jetson accelerated computing enabled cameras has solved key problems in hydroelectric dams , agriculture, and urban traffic systems. This talk will also cover the benefits and challenges of leveraging and analyzing 12 bit high fidelity imagery in diverse and challenging outdoor environments. Talk Dan Connors - Advisor, Boulder AI
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DC8161 - Developing and Deploying AI in Risk Averse Industries Learn how we overcame the odds of certifying computer vision and AI systems in an industry as risk adverse as the air traffic control sector. We use off-the-shelf cameras deployed in an airport environment to provide an out the window view of the airfield, create an enriched augmented reality view for better situational awareness, contingency and redundancy. In this talk, we take you through the steps from developing an AI using Nvidia frameworks, to deploying a camera system at an airport for air traffic control use as an imaging system as well as a tracking system using AI technology such as artificial neural networks. All the way through user acceptance tests and certification. This talk is intended as a lessons learned for your next project in smart cities or aerospace. The main focus of this talk lays on the tools used to develop AI and the tools used to understand and visualize neural networks. Talk Christian Thurow - Head of R&D, Searidge Technologies
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DC8179 - Simulation: Key to Safe Autonomous Vehicle Development Developers are training deep learning algorithms to understand an autonomous vehicle's surrounding environment and follow the rules of the road, but these algorithms still require testing and validation before the system is ready to drive on its own. Many companies have begun testing vehicles on public roads, gathering driving data and exposing the technology to real world experiences. However, this type of validation on its own can be cumbersome — the Rand Corporation estimates it would take hundreds of millions to hundreds of billions of miles driven to prove an autonomous vehicle can safely drive on its own, which translates to nearly a century of driving. Now with the power of simulation, the deep learning algorithms that act as the brain of the self-driving car can drive millions of miles in the fraction of the time it would take to drive that distance in the real world. Photorealistic simulation can create any range of driving scenarios and test them again and again to verify that the car's AI brain can safely navigate them. This session will demonstrate how virtual reality simulation is being utilized to test and validate self-driving hardware and software systems, accelerating the path to safe deployment of autonomous vehicles. Talk Curtis Beeson - Principal Scientist, NVIDIA
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DC8196 - Pittsburgh Supercomputing Center's Bridges AI: National Successes, and Introducing DGX-2 and Volta PSC's "Bridges" was the first system to successfully converge HPC, AI, and Big Data. Designed for the U.S. national research community and supported by NSF, it now serves approximately 1600 projects and 7500 users at over 350 institutions. Bridges emphasizes "nontraditional" uses that span the life, physical, and social sciences, engineering, and business, many of which are based on AI or AI-enabled simulation. We describe the characteristics of Bridges that have made it a success, and we highlight several inspirational results and how they benefited from the system architecture. We then introduce "Bridges AI", a powerful new addition for balanced AI capability and capacity that includes NVIDIA's DGX-2 and HPE NVLink-connected 8-way Volta servers. Talk Paola Buitrago - Director, AI & Big Data, PSC, Carnegie Mellon University
Nick Nystrom - Interim Director, PSC, Carnegie Mellon University
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DC8203 - Preparing Today's Youth for an AI-Powered Future Artificial intelligence is touching a growing number of jobs and will be an enormous part of the future workforce. How do we make sure today's students are equipped for a future AI-powered economy? What steps can educators and parents take now to get children excited about creating, building and innovating with AI? Learn what two youth-focused organizations, Iridescent and Girls Computing League, are doing to prepare K-12 students, including those from underserved communities, for an AI-powered future. Talk Neeyanth Kopparapu - Co-Founder and Chief Innovation Officer, Girls Computing League
Tara Chklovski - Founder and CEO, Iridescent
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DC8200 - Keynote TBA Keynote Suzette Kent - Federal Chief Information Officer, US Office of Management and Budget (Invited)
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DC8121 - Fighting the Opioid Crisis through Extreme Analytics In his talk, Todd will demonstrate how analytics at extreme speed and scale can help data scientists and analysts rapidly extract fresh insights from open and publicly available datasets related to the opioid health crisis. See how millisecond response time enables rapid and iterative geospatial exploration of this data, to help drive faster action and informed decision-making. The government produces the most socially-impactful and politically-powerful data in the world. New technologies have overcome traditional challenges of making this data both available and consumable, to help improve citizen services. For government agencies leveraging GPUs, there are new opportunities to analyze and visually interact with massive datasets without frustrating lag times. Talk Todd Mostak - CEO & Co-Founder, MapD Technologies, Inc.
DC8132 - AI at the Edge - Intelligent Machines Artificial intelligence is impacting almost every part of the industrial and agricultural supply chain. From robots that quickly adapt to build new products, to automated vehicles that address last-mile challenges for product delivery, to UAVs that can automatically detect failing infrastructure, the world is transitioning from processes that are largely manual to ones that are largely automated. We'll discuss how AI and deep learning are enabling these advances. We'll also analyze a sampling of early successes across different applications. And finally we'll describe some of the remaining challenges to wide-scale deployment, and the work NVIDIA is doing to address those challenges via its Isaac initiative. Talk Jesse Clayton - Senior Manager, Product Management for Intelligent Machines, NVIDIA
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DC8139 - Attacking the Opioid Epidemic with Exascale Genomics We will describe the CoMet application for largescale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and the Custom Correlation Coefficient are implemented using adapted xGEMM kernels optimized for GPU architectures, achieving instruction rates similar to the unmodified kernels. By aggressive overlapping of communications, transfers and computations, and accessing the tensor cores on the Volta GPU, the full computation achieves up to 95 TF per GPU (76% of tensor cores theoretical peak 125 TF) on Summit. 234 x 10^15 element comparisons and 1.88 ExaOps have been reached on 4000 nodes of Summit; full system Summit projected values are 270 x 10^15 comparisons and over 2 ExaOps. Current performance is over 10,000X beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction. Talk Daniel Jacobson - Chief Scientist for Systems Biology, Oak Ridge National Laboratory
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DC8189 - Speech Recognition Using the GPU Accelerated Kaldi Framework NVIDIA and John Hopkins have partnered up to accelerated speech recognition within the popular Kaldi framework. This framework is the de facto standard when it comes to transcoding recorded audio into the written text. Early results have shown NVIDIA GPUs can provide substantial speedups over pure CPU implementations. This talk will focus on the progress of this effort and the value that GPU acceleration adds to speech recognition. Talk Justin Luitjens - Senior Devtech Engineer, NVIDIA
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DC8190 - Accelerating Detection and Alerting of Credential Misuse Near the Edge Rules-based approaches to cyber security detection do not scale and are burdened by a reliance on human engineering. In this session, we explore machine learning approaches to cyber security threats, specifically those related to failed login attempts (often a left-of-compromise indicator of an attack) and credential misuse (abnormal behavior). Rather than apply rules, we use the data processing and analytic capabilities of the GPU Open Analytics Initiative (GOAI) to accelerate model training, inference, and other steps necessary to provide actionable alerts to an analyst in near real-time. Talk Rachel Allen - Lead Data Scientist, Booz Allen Hamilton
Bartley Richardson - Senior Data Scientist (AI Infrastructure), NVIDIA
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DLIS01 - Deployment for Intelligent Video Analytics using TensorRT

Prerequisites: Basic experience with CNNs and C++

When a trained neural network is tasked to find the answer on new data inputs, it is referred to as deployment. TensorRT is the primary tool for deployment, with various options to improve inference performance of neural networks. In this session, you'll:

  • Learn how to use giexec to run inferencing
  • Use mixed precision INT8 to optimize inferencing
  • Leverage custom layers API for plugins

Upon completion, you'll know how to use TensorRT to accelerate inferencing performance for neural networks.

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DLIS03 - DRL for Optimal Execution of Portfolio Transactions

Prerequisites: Advanced experience with neural networks and knowledge of financial industry

The "unsupervised" and "end-to-end" detection of anomalies in transactional data is one of the long-standing challenges in financial statement audits or fraud investigations. In this session, you'll explore how autoencoder neural network can be trained to detect anomalies by learning a compressed but "lossy" model of regular transactions. You'll learn:

  • Basic concepts, intuition and major building blocks of autoencoder neural networks
  • How to pre-process financial data in order to learn a model of its characteristics
  • How to design, implement, and train a deep autoencoder network using PyTorch to detect anomalies in large-scale financial data
  • How to interpret and evaluate the network's detection results as well as its reconstruction loss

Upon completion, you'll understand how to train deep autoencoder neural networks to detect anomalies in financial transactions.

Instructor-Led Training DLI certified instructor
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DLIS07 - Image Super Resolution using Autoencoders

Prerequisites: Experience with CNNs

Learn how to leverage the power of a neural network with autoencoders to create high-quality images from low-quality source images. In this session, you'll:

  • Understand and design an autoencoder
  • Train and run a model to produce high-quality images from low-quality ones
  • Learn various methods to rigorously measuring image quality

Upon completion, you'll be able to use deep learning to create high-quality images for a variety of purposes.

Instructor-Led Training DLI certified instructor
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DC8103 - Simplifying AI for Communications, Radar, and Wireless Systems Deep learning continues to show benefit in significant aspects of sensor systems including computer vision, speech recognition, and cybersecurity. In parallel, radio frequency (RF) systems have become increasingly complex and the number of connected devices will significantly increase as IoT and 5G systems become prevalent. Deep learning within RF systems is a new field of research that shows promise for dealing with a congested spectrum, brining reliability enhancements, and simplifying the ability to build effective signal processing systems. The utilization of deep learning algorithms within RF technology has shown superior results and the ability to classify signals well below the noise floor when compared to traditional signal processing methods. Working with strategic partners, we have designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA Jetson TX2. We discuss RF specific system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we will present data demonstrating applications in the field of deep learning enabled RF systems. Talk John Ferguson - CEO, Deepwave Digital
DC8155 - AI Infrastructure for Healthcare Deployments As research and clinical healthcare organizations formulate and Implement AI strategies, a crucial component is planning for the proper AI compute infrastructure. This talk will address compute infrastructure planning in healthcare settings, including reference architectures and best practices that NVIDIA has developed based on our internal AI supercomputer, as well as examples of successful AI deployments by leading healthcare organizations. Talk Pradeep Gupta - Head Solutions Architect, NVIDIA
Pradeep Gupta - Head Solutions Architect, NVIDIA
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DC8159 - Personalized Deep Learning with Incremental Adaptation Learn how Lilt automatically adapts neural machine translation models in real time to match translator word choice and writing style using sparse gradient updates of residual tensors. This presentation will describe recent research results and practical production tips that apply to a wide variety of interactive machine learning systems that offer personalized results. Talk Joern Wuebker - Senior Research Scientist, Lilt
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DC8160 - GPUs and AI in Agriculture: Optimize Every Plant, Dramatically Reduce Chemical Usage This presentation will provide an overview of Blue River Technology's use of GPUs in developing their See and Spray technology for Precision Agriculture. We will motivate the use of Deep Learning in detection and classification of crops and weeds in production environments, and highlight the ways in which NVIDIA GPUs have provided the tools and platform for training powerful models. NVIDIA GPUs have also helped us perform real-time inference on working machines in the field. This talk will show how these systems perform and provide videos of the machines in operation. Talk Jim Ostrowski - VP, Engineering, Blue River Technology (John Deere)
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DC8176 - Content, Authorship, and Infrastructure for VR in Educational Settings The potential for XR technologies to transform education is well-recognized. Even so, many practical barriers remain before any of them can be widely adopted in practice. In this talk, I will present a multi-stage pipeline model spanning from conception to adoption of a VR learning experience, noting challenges and approaches relevant to each stage. I will use my own work in K12 (domain-independent project-based learning) and higher education (electricity and magnetism) to exemplify the arguments. Talk Scott Greenwald - Research Scientist, MIT Media Lab, Fluid Interfaces Group
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