Prerequisites: Familiarity with the basic programming fundamentals, such as functions and variables

Frameworks: Caffe, DIGITS

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

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

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

Prerequisites: Basic experience with neural networks and Python programming, familiarity with linguistics

Frameworks: TensorFlow, Keras

Certification: Available upon completion of code-based and multiple choice assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Learn the latest deep learning techniques to understand 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, you’ll be proficient in NLP using embeddings in similar applications.

Prerequisites: Experience with stochastic gradient descent mechanics

Frameworks: TensorFlow

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU, or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This workshop will teach you how to use multiple GPUs to training neural networks. You'll learn:

• Approaches to multi-GPU training
• Algorithmic and engineering challenges to large-scale training
• Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.

Prerequisites: Experience with deep networks (specifically variations of CNNs), intermediate-level experience with C++ and Python

Frameworks: TensorFlow, TensorRT, Caffe

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

With the increase in traffic cameras, growing prospect of autonomous vehicles, and promising outlook of smart cities, there's a rise in demand for faster and more efficient object detection and tracking models. This involves identification, tracking, segmentation and prediction of different types of objects within video frames.

In this workshop, you’ll learn how to:

• Efficiently process and prepare video feeds using hardware accelerated decoding methods
• Train and evaluate deep learning models and leverage ""transfer learning"" techniques to elevate efficiency and accuracy of these models and mitigate data sparsity issues
• Explore the strategies and trade-offs involved in developing high-quality neural network models to track moving objects in large-scale video datasets
• Optimize and deploy video analytics inference engines by acquiring DeepStream SDK and TensorRT optimization tools

Upon completion, you'll be able to design, train, test and deploy building blocks of a hardware-accelerated traffic management system based on parking lot camera feeds.

Prerequisites: Basic familiarity with deep neural networks, Basic coding experience in Python or a similar language

Frameworks: ROS, DIGITS, Jetson

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

AI is revolutionizing the acceleration and development of robotics across a broad range of industries. Explore how to create robotics solutions on a Jetson for embedded applications. You’ll learn how to:

• Apply computer vision models to perform detection
• Prune and optimize the model for embedded application
• Train a robot to actuate the correct output based on the visual input

Upon completion, you’ll know how to deploy high-performance deep learning applications for robotics.

Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs. CUDA programming knowledge is not required.

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

• Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
• Use Numba to create and launch custom CUDA kernels
• Apply key GPU memory management techniques

Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

At this reception, meet NVIDIA staff and other GTC alumni to get tips, especially if you're a first-timer.

Prerequisites: Basic experience with C/C++

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

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

• Exposing the parallelization of CPU-only applications, and refactoring them to run in parallel on GPUs
• Successfully managing memory
• Utilizing CUDA parallel thread hierarchy to further increase performance

Upon completion, you'll be able to utilize CUDA to accelerate your CPU-only C/C++ applications for massive performance gains.

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

• Loading image data to a training environment
• Choosing and training a network
• Testing with new data and iterating to improve performance

Upon completion, you'll be able to assess what data you should be using for training.

Prerequisites: Basic experience with neural networks

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. Learn how to segment MRI images to measure parts of the heart by:

• Comparing image segmentation with other computer vision problems
• Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API
• Learning to implement effective metrics for assessing model performance

Upon completion, you'll be able to set up most computer vision workflows using deep learning.

Prerequisites: Basic CUDA-C and C++ coding skills.

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Learn to maximize productivity when developing software for the Jetson platform. We'll explain how to manage source code on the host PC to cross-compile the software and initiate remote debugging sessions to debug CPU C/C++ and CUDA C code. We'll use a comprehensive set of exercises to teach you how to use NVIDIA's new suite of tools — Nsight Compute for optimizing CUDA kernels, Nsight Systems for optimizing CPU code and tracing multi-process system-wide activities, and Nsight Graphics for debugging and profiling 3D graphics applications.

Prerequisites: None

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive. Learn how to use GPUs to accelerate the process of building a portfolio of securities that will perform well in the market. We'll describe our big data algorithm in Part 1, which uses a Sharpe Ratio on the full list of NYSE and NASDAQ candidate securities as a performance metric. We will outline a GPU kernel that we designed to measure the first two moments of daily log-returns and combine them. We then select top performers. In Part 2 we use deep learning in the form of a feed forward neural network with Keras and Tensorflow to train on income statements for securities in order to predict whether they will outperform the S&P 500 Index near term. In our lab we are able to examine each of these techniques and observe the GPU acceleration.

After the Big Bang, the Universe contained hydrogen, helium, and a bit of lithium. Every other element on the periodic table is produced in stars and is disseminated into interstellar space via supernova explosions. Simulations of supernovae are among the most compute-intensive multi-physics applications on the world's largest modern supercomputers. We will discuss recent development of the FLASH code intended to make these simulations even more physically meaningful. In particular, we’ll describe how our work on FLASH, as part of the OLCF CAAR program, allowed us to increase the number of tracked nuclear species from about a dozen to hundreds, making precision predictions that can be compared to observations possible.

Exploring the Best Server for AI
Speaker: Samuel D. Matzek, Sr. Software Engineer
Speaker: Maria Ward, IBM Accelerated Server Offering Manager

Explore the server at the heart of the Summit and Sierra supercomputers, and the best server for AI. We will discuss the technical details that set this server apart and why it matters for your machine learning and deep learning workloads.

IBM Cloud for AI at Scale
Speaker: Alex Hudak, IBM Cloud Offering Manager

AI is fast changing the modern enterprise with new applications that are resource demanding, but provide new capabilities to drive insight from customer data. IBM Cloud is partnering with NVIDIA to provide a world class and customized cloud environment to meet the needs of these new applications. Learn about the wide range of NVIDIA GPU solutions inside the IBM Cloud virtual and bare metal server portfolio, and how customers are using them across Deep Learning, Analytics, HPC workloads, and more.

IBM Spectrum LSF Family Overview & GPU Support
Speaker: Larry Adams, Global Architect - Cross Sector, Developer, Consultant, IBM Systems

How to Fuel the Data Pipeline
Speaker: Kent Koeninger, IBM

IBM Storage Reference Architecture for AI with Autonomous Driving
Speaker: Kent Koeninger, IBM

This session will present a human-centered paradigm for building autonomous vehicle systems, contrasting it with the current industry approach. We will include discussion and video demonstration of new work at the Massachusetts Institute of Technology on driver-state sensing, voice-based transfer of control, annotation of large-scale naturalistic driving data, and the challenges of building and testing a human-centered autonomous vehicle.

Red Hat Virtualization is an open platform that is built on Kernel-based Virtual Machine (KVM), one of several hypervisors supporting NVIDIA vGPU integration. Learn about RHV installation, the NVIDIA vGPU host driver, deployment of guest VMs with single and multiple vGPU enablement, as well as NVIDIA GRID license manager.

We'll discuss network quantization — its background, methods, achievements, and the motivation behind it. Deep neural networks have achieved remarkable performance in a wide range of tasks. But DNNs are computationally intensive and resource-consuming, which hinders their use in embedded systems. We'll explain how we're working to alleviate this problem with quantized neural networks and a lightweight framework for efficient inference of these networks.

We'll discuss Substance Alchemist, a tool that will allow users to manage material collections and create new materials from pictures, scans, or pre-existing materials. We will detail the different facets of Alchemist and explain how GPU-Accelerated AI will enhance material creation, with a deep dive into the delighter features that leverage TensorCore. The tool was first demonstrated at SIGGRAPH 2018.

Learn how to prepare pathology whole-slide imaging for a machine learning experiment using open-source tools.

We'll discuss how we're working to enable safe, reliable autonomous transport by developing highly accurate, real-time 3D views around autonomous vehicles. Our perception sensor suite uses RADAR, LIDAR, cameras, and IMUs to provide a 360-degree safety shield. We'll explain how data from high-performance imaging RADAR, LIDAR, and cameras are fused together to give the vehicle its sense of sight, while the IMU gives the vehicle its sense of feeling and ensures it maintains its heading. The large amount of data generated by our Drive360 sensors requires in-vehicle high performance AI computers to create a real-time 3D view around the vehicle.

Artificial intelligence and the latest in computer vision techniques are quickly re-shaping the future of retail. By deploying modern deep learning techniques, technology companies are improving the overall retail shopping experience by getting rid of slow, cumbersome checkout lines. We'll talk about our work on autonomous checkout, which can make shopping a seamless, magical and more human interaction. Standard Cognition, along with other technology innovators like AmazonGo, have announced plans to deploy thousands of autonomous checkout-enabled retail stores by 2021.

We'll present a deep learning-based analysis framework for making key decisions about heart valve replacement and valve design. We'll describe how we use deep learning to predict valve performance measures, which makes these measurements accessible to physicians who lack expert computational knowledge. We will explain how our trained DL framework can be used interactively to predict valve-performance measures with the same fidelity as time-consuming biomechanics simulations. We'll also discuss how our tool can help doctors with heart valve diagnosis, ultimately improving patient care.

Manufacturers are increasingly adopting AI to improve productivity. We'll discuss our work to automate the inspection process, which represent 20 percent of the manufacturing pipeline. We're developing deep learning for automated visual inspection, aiming for human-level accuracy using NVIDIA GPUs and TensorRT to deploy the neural network on Jetson AGX Xavier. We'll also introduce our other new deep learning products.