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            NVIDIA Deep Learning Institute

            Training You to Solve the World’s Most Challenging Problems

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            The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Developers, data scientists, researchers, and students can get practical experience powered by GPUs in the cloud and earn a certificate of competency to support professional growth. We offer self-paced, online training for individuals, instructor-led workshops for teams, and downloadable course materials for university educators.

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              INDIVIDUALS

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              UNIVERSITIES

            For self-learners, students, and teams of less than 20 developers, we recommend self-paced online training to learn how to apply deep learning to your projects and how to accelerate your applications with CUDA and OpenACC . You’ll gain practical skills for your work and earn a certificate of subject matter competency. You can also attend an upcoming instructor-led workshop in your area.

            Online training with DLI

            Dive into self-paced online training from anywhere at any time, with access to a fully-configured, GPU-accelerated workstation in the cloud. Choose an 8-hour course to implement and deploy an end-to-end project or a 2-hour course to apply a specific technology or technique.

            Certificate Available

            Deep Learning Courses

            DEEP LEARNING FUNDAMENTALS

            • Fundamentals of Deep Learning for Computer Vision 

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

              Tools and Frameworks: Caffe, DIGITS

              Assessment Type: Code-based

              Duration: 8 hours

              Languages: English, Japanese, Korean, Simplified Chinese, Traditional Chinese

              Price: $90

              Certificate Available

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

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

            • Getting Started with AI on Jetson Nano

              Prerequisites: Familiarity with Python (helpful, not required)

              Libraries, Tools and Frameworks: PyTorch, Jetson Nano

              Duration: 8 hours

              Languages: English

              Price: Free

              Certificate: Available

              The power of AI is now in the hands of makers, self-taught developers, and embedded technology enthusiasts everywhere with the NVIDIA Jetson Nano Developer Kit. This easy-to-use, powerful computer lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. In this course, you'll use Jupyter iPython notebooks on your own Jetson Nano to build a deep learning classification project with computer vision models.

              You'll learn how to:

              • Set up your Jetson Nano and camera
              • Collect image data for classification models
              • Annotate image data for regression models
              • Train a neural network on your data to create your own models
              • Run inference on the Jetson Nano with the models you create

              Upon completion, you'll be able to create your own deep learning classification and regression models with the Jetson Nano. Hardware is required to complete this course (view details).

            • Image Classification with DIGITS

              Prerequisites: None

              Tools and Frameworks: Caffe (with DIGITS interface)

              Duration: 2 hours

              Languages: English, Japanese, Simplified Chinese

              Price: $30

              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.

            • Object Detection with DIGITS

              Prerequisites: Basic experience with neural networks

              Tools and Frameworks: Caffe (with DIGITS interface)

              Duration: 2 hours

              Languages: English, Simplified Chinese

              Price: $30

              Learn to apply deep learning to object detection through the challenge of detecting whale faces from aerial images by:

              • Combining traditional computer vision with deep learning
              • Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe
              • Harnessing the knowledge of the deep learning community by identifying and using a purpose-built network and end-to-end labeled data

              Upon completion, you’ll be able to solve custom problems with deep learning.

            • Optimization and Deployment of TensorFlow Models with TensorRT

              Prerequisites: Experience with TensorFlow and Python

              Tools and Frameworks: TensorFlow, Python, TensorRT (TF-TRT)

              Duration: 2 hours

              Languages: English

              Price: $30

              Learn the fundamentals of generating high-performance deep learning models in the TensorFlow platform using built-in TensorRT library (TF-TRT) and Python. You'll explore:

              • How to pre-process classifications models and freeze graphs and weights in order to perform optimization
              • Get familiar with fundamentals of graph optimization and quantization using FP32, FP16 and INT8
              • Use TF-TRT API to optimize subgraphs and select optimization parameters that best fit your model
              • Design and embed custom operations in Python to mitigate the non-supporting layers problem and optimize detection models

              Upon completion, you'll understand how to utilize TF-TRT to achieve deployment-ready optimized models.

            • Accelerating Data Science Workflows with RAPIDS

              Prerequisites: Advanced competency in Pandas, NumPy, and scikit-learn

              Tools and Frameworks: None

              Duration: 2 hours

              Languages: English

              Price: $30

              The open source RAPIDS project allows data scientists to GPU-accelerate their data science and data analytics applications from beginning to end, creating possibilities for drastic performance gains and techniques not available through traditional CPU-only workflows.

              Learn how to GPU-accelerate your data science applications by:

              • Utilizing key RAPIDS libraries like cuDF (GPU-enabled Pandas-like dataframes) and cuML (GPU-accelerated machine learning algorithms)
              • Learning techniques and approaches to end-to-end data science, made possible by rapid iteration cycles created by GPU acceleration
              • Understanding key differences between CPU-driven and GPU-driven data science, including API specifics and best practices for refactoring

              Upon completion, you'll be able to refactor existing CPU-only data science workloads to run much faster on GPUs and write accelerated data science workflows from scratch.

            • Deep Learning at Scale with Horovod

              Prerequisites: Competency in Python and professional experience training deep learning models in Python

              Tools and Frameworks: Horovod, TensorFlow, Keras, Python

              Duration: 2 hours

              Languages: English

              Price: $30

              Learn how to scale deep learning training to multiple GPUs with Horovod, the open-source distributed training framework originally built by Uber and hosted by the LF AI Foundation. In this course, you'll:

              • Complete a step-by-step refactor of a Fashion-MNIST classification model to use Horovod and run on four NVIDIA V100 GPUs
              • Understand Horovod's MPI roots and develop an intuition for parallel programming motifs like multiple workers, race conditions, and synchronization
              • Use techniques like learning rate warmups that greatly impact scaled deep learning performance

              Upon completion, you'll be able to use Horovod to effectively scale deep learning training in new or existing code bases.

            • Image Segmentation with TensorFlow

              Prerequisites: Basic experience with neural networks

              Tools and Frameworks: TensorFlow

              Duration: 2 hours

              Languages: English

              Price: $30

              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.

            • Signal Processing with DIGITS

              Prerequisites: Basic experience training neural networks

              Tools and Frameworks: Caffe, DIGITS

              Duration: 2 hours

              Languages: English, Simplified Chinese

              Price: $30

              Deep neural networks are better at classifying images than humans, which has implications beyond what we expect of computer vision. Learn how to convert radio frequency (RF) signals into images to detect a weak signal corrupted by noise. You’ll be trained how to:

              • Treat non-image data as image data
              • Implement a deep learning workflow (load, train, test, adjust) in DIGITS
              • Test performance programmatically and guide performance improvements

              Upon completion, you’ll be able to classify both image and image-like data using deep learning.

            DEEP LEARNING FOR DIGITAL CONTENT CREATION

            • Image Style Transfer with Torch

              Prerequisites: Experience with CNNs

              Tools and Frameworks: Torch

              Duration: 2 hours

              Languages: English

              Price: $30

              Explore how to transfer the look and feel of one image to another image by extracting distinct visual features. See how convolutional neural networks (CNNs) are used for feature extraction, and how these features feed into a generator to create a new image. You’ll learn how to:

              • Transfer the look and feel of one image to another image by extracting distinct visual features
              • Qualitatively determine whether a style is transferred correctly using different techniques
              • Use architectural innovations and training techniques for arbitrary style transfer

              Upon completion, you’ll be able to use neural networks for arbitrary style transfer at a speed that's effective for video.

            • Rendered Image Denoising Using Autoencoders

              Prerequisites: Experience with CNNs

              Tools and Frameworks: TensorFlow

              Duration: 2 hours

              Languages: English

              Price: $30

              Learn how neural networks with autoencoders can be used to dramatically speed up the removal of noise in ray traced images. You’ll learn how to:

              • Determine whether noise exists in rendered images
              • Use a pre-trained network to denoise some sample images or your own images
              • Train your own denoiser using the provided dataset

              Upon completion, you’ll be able to use autoencoders inside neural networks to train your own rendered image denoiser.

            • Image Super Resolution Using Autoencoders

              Prerequisites: Experience with CNNs

              Tools and Frameworks: Keras

              Duration: 2 hours

              Languages: English, Simplified Chinese

              Price: $30

              Leverage the power of a neural network with autoencoders to create high-quality images from low-quality source images. In this mini course, you'll:

              • Understand and design an autoencoder
              • Learn various methods to rigorously measuring image quality

              Upon completion, you'll be able to use autoencoders inside neural networks to significantly enhance image quality.

            DEEP LEARNING FOR HEALTHCARE

            • Modeling Time Series Data with Recurrent Neural Networks in Keras

              Prerequisites: Basic experience with deep learning

              Tools and Frameworks: Keras

              Duration: 2 hours

              Languages: English

              Price: $30

              Recurrent Neural Networks (RNNs) allow models to classify or forecast time-series data, like natural language, markets, and even a patient’s health over time. You'll learn how to:

              • Create training and testing datasets using electronic health records in HDF5 (hierarchical data format version five)
              • Prepare datasets for use with recurrent neural networks, which allows modeling of very complex data sequences
              • Construct a Long-Short Term Memory model (LSTM), a specific RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data

              Upon completion, you’ll be able to model time-series data using RNNs.

            • Medical Image Classification Using the MedNIST Dataset

              Prerequisites: Basic experience with Python

              Tools and Frameworks: PyTorch

              Duration: 2 hours

              Languages: English,  Simplified Chinese

              Price: $30

              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.

            • Data Science Workflows for Deep Learning in Medical Applications

              Prerequisites: Basic experience with Python and CNNs

              Tools and Frameworks: PyTorch

              Duration: 2 hours

              Languages: English

              Price: $30

              Medical datasets present special challenges for the application of deep learning. You will:

              • Learn introductory techniques in data augmentation and standardization
              • Experiment with these techniques on a simple medical imaging dataset
              • Validate your techniques by training a convolutional neural network on the augmented dataset

              Upon completion, you'll be able to apply simple data manipulation techniques to your medical imaging datasets.

            • Medical Image Segmentation with DIGITS

              Prerequisites: Basic experience with CNNs and Python

              Tools and Frameworks: DIGITS, Caffe

              Duration: 2 hours

              Languages: English

              Price: $30

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

              • Extending Caffe with custom Python layers
              • Implementing the process of transfer learning
              • Creating fully convolutional neural networks (CNNs) from popular image classification networks

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

            • Image Classification with TensorFlow: Radiomics—1p19q Chromosome Status Classification

              Prerequisites: Basic experience with CNNs and Python

              Tools and Frameworks: TensorFlow

              Duration: 2 hours

              Languages: English, Simplified Chinese

              Price: $30

              Thanks to work being performed at the Mayo Clinic, using deep learning techniques to detect radiomics from MRI imaging has led to more effective treatments and better health outcomes for patients with brain tumors. Learn to detect the 1p19q co-deletion biomarker by:

              • Designing and training convolutional neural networks (CNNs)
              • Using imaging genomics (radiomics) to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy
              • Exploring the radiogenomics work being done at the Mayo Clinic

              Upon completion, you’ll have unique insight into the novelty and promising results of using deep learning to predict radiomics.

            • Medical Image Analysis with R and MXNet

              Prerequisites: Basic experience with CNNs and Python

              Tools and Frameworks: MXNet

              Duration: 2 hours

              Languages: English

              Price: $30

              Convolutional neural networks (CNNs) can be applied to medical image analysis to infer patient status from non-visible images. Learn how to train a CNN to infer the volume of the left ventricle of the human heart from time-series MRI data. You'll explore how to:

              • Extend a canonical 2D CNN to more complex data
              • Use MXNet through the standard Python API and R
              • Process high-dimensionality imagery that may be volumetric and have a temporal component

              Upon completion, you’ll know how to use CNNs for non-visible images.

            • Data Augmentation and Segmentation with Generative Networks for Medical Imaging

              Prerequisites: Experience with CNNs

              Tools and Frameworks: TensorFlow

              Duration: 2 hours

              Languages: English

              Price: $30

              A generative adversarial network (GAN) is a pair of deep neural networks: a generator that creates new examples based on the training data provided and a discriminator that attempts to distinguish between genuine and simulated data. As both networks improve together, the examples created become increasingly realistic. This technology is promising for healthcare, because it can augment smaller datasets for training of traditional networks. You'll learn how to:

              • Generate synthetic brain MRIs
              • Apply GANs for segmentation
              • Use GANs for data augmentation to improve accuracy

              Upon completion, you'll be able to apply GANs to medical imaging use cases.

            • Coarse-to-Fine Contextual Memory for Medical Imaging

              Prerequisites: Experience with CNNs and long short term memory (LSTMs)

              Tools and Frameworks: TensorFlow

              Duration: 2 hours

              Languages: English

              Price: $30

              Coarse-to-fine contextual memory (CFCM) is a technique developed for image segmentation using very deep architectures and incorporating features from many different scales with convolutional long short-term memory (LSTM). You’ll:

              • Take a deep dive into encoder-decoder architectures for medical image segmentation
              • Get to know common building blocks (convolutions, pooling layers, residual nets, etc.)
              • Investigate different strategies for skip connections

              Upon completion, you'll be able to apply CFCM techniques to medical image segmentation and similar imaging tasks.

            DEEP LEARNING FOR INTELLIGENT VIDEO ANALYTICS

            • AI Workflows for Intelligent Video Analytics with DeepStream

              Prerequisites: Experience with C++ and Gstreamer

              Tools and Frameworks: DeepStream3

              Duration: 2 hours

              Languages: English

              Price: $30

              The DeepStream 3.0 framework features hardware-accelerated building blocks of Intelligent Video Analytics (IVA) applications. This allows developers to focus on building core deep learning networks. The DeepStream SDK underpins a variety of use cases and offers flexibility on the deployment medium.

              You’ll learn how to:

              • Deploy DeepStream pipeline for parallel, multi-stream video processing and deliver applications with maximum throughput at scale
              • Configure the processing pipeline and create intuitive, graph-based applications. Leverage multiple deep network models to process video streams and achieve more intelligent insights

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

            Accelerated Computing Courses

            • Fundamentals of Accelerated Computing with CUDA C/C++ 

              Prerequisites: Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations.

              Assessment Type: Code-based

              Duration: 8 hours

              Languages: English, Japanese, Korean, Simplified Chinese, Traditional Chinese

              Price: $90

              Certificate Available

              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, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques.

            • Fundamentals of Accelerated Computing with CUDA Python

              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.

              Assessment Type: Code-based

              Duration: 8 hours

              Languages: English

              Price: $90

              Certificate Available

              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.

            • Fundamentals of Accelerated Computing with OpenACC

              Prerequisites: Basic experience with C/C++

              Duration: 8 hours

              Languages: English

              Price: $90

              Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this course, you’ll learn:

              • Four simple steps to accelerating your already existing application with OpenACC
              • How to profile and optimize your OpenACC codebase
              • How to program on multi-GPU systems by combining OpenACC with the message passing interface (MPI)

              Upon completion, you’ll be able to build and optimize accelerated heterogeneous applications on multiple GPU clusters using a combination of OpenACC, CUDA-aware MPI, and NVIDIA profiling tools.

            • High-Performance Computing with Containers

              Prerequisites: Proficiency programming in C/C++ and professional experience working on HPC applications

              Tools and Frameworks: Docker, Singularity, HPCCM, C/C++

              Duration: 2 hours

              Languages: English

              Price: $30

              Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for high-performance computing (HPC) application development. In this course, you'll:

              • Explore the basics of building and running Docker and Singularity containers
              • Use the HPC Container Maker (HPCCM) to programmatically configure complex, portable, bare-metal HPC environments for your application
              • Apply advanced container building techniques like layered containers and multi-stage builds
              • Utilize drop-in containerized versions of existing HPC applications like MPI Bandwidth and MILC

              Upon completion, you'll be able to quickly build and utilize Docker, Singularity, and HPCCM for portable, bare-metal performance in your HPC applications.

            • Accelerating Applications with CUDA C/C++

              Prerequisites: Basic experience with C/C++

              Duration: 2 hours

              Languages: English, Japanese

              Price: $30

              Learn how to accelerate your C/C++ application using CUDA to harness the massively parallel power of NVIDIA GPUs. You'll learn how to program with CUDA in order to:

              • Accelerate SAXPY algorithms
              • Accelerate Matrix Multiply algorithms
              • Accelerate heat conduction algorithms

              Upon completion, you'll be able to use the CUDA platform to accelerate C/C++ applications.

            • OpenACC – 2X in 4 Steps

              Prerequisites: Basic experience with C/C++

              Duration: 2 hours

              Languages: English

              Price: $30

              Learn how to accelerate your C/C++ or Fortran application using OpenACC to harness the massively parallel power of NVIDIA GPUs. OpenACC is a directive-based approach to computing where you provide compiler hints to accelerate your code, instead of writing the accelerator code yourself. Get started on the four-step process for accelerating applications using OpenACC:

              • Characterize and profile your application
              • Add compute directives
              • Add directives to optimize data movement
              • Optimize your application using kernel scheduling

              Upon completion, you will be ready to use a profile-driven approach to rapidly accelerate your C/C++ applications using OpenACC directives.

            • GPU Memory Optimizations with CUDA C/C++

              Prerequisites: Basic experience accelerating applications with CUDA C/C++

              Duration: 2 hours

              Languages: English

              Price: $30

              Explore memory optimization techniques for programming with CUDA C/C++ on an NVIDIA GPU, and how to use the NVIDIA Visual Profiler (NVVP) to support these optimizations. You'll learn how to:

              • Implement a naive matrix transposing algorithm
              • Perform several cycles of profiling the algorithm with NVVP and optimize its performance

              Upon completion, you'll know how to analyze and improve global and shared memory access patterns, and how to optimize your accelerated C/C++ applications.

            • Accelerating Applications with GPU-Accelerated Libraries in C/C++

              Prerequisites: Basic experience accelerating applications with CUDA C/C++

              Duration: 2 hours

              Languages: English, Japanese

              Price: $30

              Learn how to accelerate your C/C++ application using drop-in libraries to harness the massively parallel power of NVIDIA GPUs. You'll work through three exercises, including how to:

              • Use cuBLAS to accelerate a basic matrix multiply
              • Combine libraries by adding some cuRAND API calls to the previous cuBLAS calls
              • Use nvprof to profile code and optimize with some CUDA Runtime API calls

              Upon completion, you'll be ready to utilize several CUDA enabled libraries for rapid application acceleration in your existing CPU-only C/C++ programs.

            • Using Thrust to Accelerate C++

              Prerequisites: Basic experience accelerating applications with CUDA C/C++

              Duration: 2 hours

              Languages: English

              Price: $30

              Thrust is a parallel algorithms library loosely based on the C++ Standard Template Library. It enables developers to quickly embrace the power of parallel computing and supports multiple system back-ends such as OpenMP and Intel's Threading Building Blocks. Use Thrust to accelerate C++ through exercises that cover:

              • Basic Iterators, Containers, and Functions
              • Built-in and Custom Functors
              • Portability to CPU processing

              Upon completion, you'll be ready to harness the power of the Thrust library to accelerate your C/C++ applications.

            Online Training with Partners

            DLI collaborates with leading educational organizations to expand the reach of deep learning training to developers worldwide.