NVIDIA Deep Learning Institute Online Labs

ONLINE SELF-PACED TRAINING

The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning and accelerated computing.

Choose from full-day courses to deploy an end-to-end project, or two-hour electives to learn a specific technology or technique.

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Fundamentals of Deep Learning for Computer Vision

Prerequisites: None
Cost: $90

Get certified! Complete the assessment at the end of this course.

Explore the fundamentals of deep learning: train neural networks and use results to improve performance and capabilities.

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Fundamentals of Accelerated Computing with CUDA C/C++

Prerequisites: Basic experience with C/C++
Cost: $90

Get certified! Complete the assessment at the end of this course.

Accelerate your C/C++ applications on the massively parallel NVIDIA GPUs using CUDA.

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Fundamentals of Accelerated Computing with CUDA Python

Prerequisites: Basic experience with Python and NumPy
Cost: $90

Explore how to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

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Fundamentals of Accelerated Computing with OpenACC

Prerequisites: Basic experience with C/C++
Cost: $90

Learn the basics of OpenACC, a high-level programming language for programming on GPUs.

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Image Classification with DIGITS

Prerequisites: None
Cost: FREE

Learn how to train a deep neural network to recognize handwritten digits by loading image data to a training environment, choosing and training a network, and testing with new data and iterating to improve performance.

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Object Detection with DIGITS

Prerequisites: None
Cost: FREE

Learn how to detect objects using computer vision and deep learning by identifying a purpose-built network and using end-to-end labeled data.

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Modeling Time Series Data with Recurrent Neural Networks in Keras

Prerequisites: Basic experience with deep learning
Cost: FREE

Explore how to classify and forecast time-series data using RNNs, such as modeling a patient’s health over time.

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Neural Network Deployment with DIGITS and TensorRT

Prerequisites: Basic experience with neural networks
Cost: $30

Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time.

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Deep Learning Workflows with TensorFlow, MXNet, and NVIDIA-Docker

Prerequisites: Basic experience with a bash terminal
Cost: $30

Learn how to use the NVIDIA Docker plugin to containerize production-grade deep learning workflows using GPUs.

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Image Segmentation with TensorFlow

Prerequisites: Basic experience with neural networks
Cost: $30

Learn to combine computer vision and natural language processing to describe scenes using deep learning.

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Image Classification with Microsoft Cognitive Toolkit

Prerequisites: None
Cost: $30

Learn to train a neural network using the Microsoft Cognitive Toolkit framework.

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Linear Classification with TensorFlow

Prerequisites: None
Cost: $30

Learn how to make predictions from structured data using TensorFlow’s TFLearn API.

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Signal Processing Using DIGITS

Prerequisites: Basic experience with neural networks
Cost: $30

Learn how to convert Radio Frequency (RF) signals into images to detect a weak signal corrupted by noise.

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Image Creation using GANs with TensorFlow and DIGITS

Prerequisites: Experience with CNNs
Cost: $30

Discover how to train a Generative Adversarial Network (GAN) to generate image contents in DIGITS.

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Image Style Transfer with Torch

Prerequisites: Experience with CNNs
Cost: $30

Learn how to transfer the look and feel of one image to another image by extracting distinct visual features using convolutional neural networks (CNNs).

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Rendered Image Denoising using Autoencoders

Prerequisites: Experience with CNNs
Cost: $30

Explore how a neural network with an autoencoder can be used to dramatically speed up the removal of noise in ray traced images.

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Image Super Resolution using AutoEncoders

Prerequisites: Experience with CNNs
Cost: $30

Leverage the power of a neural network with autoencoders to create high-quality images from low-quality source images.

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Deployment for Intelligent Video Analytics using TensorRT

Prerequisites: Basic experience with CNNs and C++
Cost: $30

Learn how to use TensorRT to accelerate inferencing performance for neural networks.

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Accelerating Applications with CUDA C/C++

Prerequisites: Basic experience with C/C++
Cost: FREE

Learn how to accelerate your C/C++ application using CUDA to harness the massively parallel power of NVIDIA GPUs.

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OpenACC – 2X in 4 Steps

Prerequisites: Basic experience with C/C++
Cost: FREE

Learn how to accelerate C/C++ or Fortran applications using OpenACC to harness the massively parallel power of NVIDIA GPUs.

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Introduction to Accelerated Computing

Prerequisites: None
Cost: $30

Explore a variety of techniques for accelerating applications, including CUDA and OpenACC.

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GPU Memory Optimizations with CUDA C/C++

Prerequisites: Basic experience with C/C++
Cost: $30

Learn useful 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.

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

Prerequisites: “Accelerating Applications with CUDA C/C++” or similar experience
Cost: $30

Learn how to accelerate your C/C++ application using CUDA optimized libraries to harness the massively parallel power of NVIDIA GPUs.

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Accelerating Applications with GPU-Accelerated Libraries in Python

Prerequisites: Basic experience with Python
Cost: $30

Learn how to accelerate your Python application using CUDA optimized libraries to harness the massively parallel power of NVIDIA GPUs.

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Using Thrust to Accelerate C++

Prerequisites: “Accelerating Applications with CUDA C/C++” or similar experience
Cost: $30

Discover how to build GPU-accelerated applications in C/C++ that utilize the powerful Thrust library.

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Profiling and Parallelizing with OpenACC

Prerequisites: "OpenACC - 2X in 4 Steps" or similar experience
Cost: $30

Get hands-on experience with the first two steps of the OpenACC programming cycle.

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Expressing Data Movement and Optimizing Loops with OpenACC

Prerequisites: “Profiling and Parallelizing with OpenACC” or similar experience
Cost: $30

Learn how to add data management and loop directives to optimize OpenACC accelerated code.

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Introduction to Multi-GPU Programming with MPI and OpenACC

Prerequisites: "OpenACC - 2X in 4 Steps" or similar experience
Cost: $30

Explore how to program multi-GPU systems or GPU clusters using the Message Passing Interface (MPI) and OpenACC.

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Advanced Multi-GPU Programming with MPI and OpenACC

Prerequisites: “Introduction to Multi-GPU Programming with MPI and OpenACC” or similar experience
Cost: $30

Learn how to improve multi-GPU MPI+OpenACC programs by overlapping communication with computation and handling noncontiguous halo updates.

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Pipelining Work on the GPU with OpenACC

Prerequisites: “Expressing Data Movement and Optimizing Loops with OpenACC” or similar experience
Cost: $30

Learn how to optimize data copies in OpenACC applications to overlap with GPU computation using a simple technique called pipelining.

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Profile-Driven Approach to Accelerate Seismic Application with OpenACC

Prerequisites: None
Cost: $30

Learn how to use PGPROF, a host and GPU profiling tool, with OpenACC to accelerate your C/C++ applications.

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Accelerating Applications with CUDA Fortran

Prerequisites: Basic experience with Fortran
Cost: $30

Learn how to accelerate your Fortran application using CUDA to harness the massively parallel power of NVIDIA GPUs.

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GPU Memory Optimizations with CUDA Fortran

Prerequisites: “Accelerating Applications with CUDA Fortran” or similar experience
Cost: $30

Discover useful memory optimization techniques for programming with CUDA Fortran on an NVIDIA GPU, and how to use the NVIDIA Visual Profiler (NVVP) to support these optimizations.

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Accelerating Applications with GPU-Accelerated Libraries with Fortran

Prerequisites: Basic experience with Fortran
Cost: $30

Learn how to accelerate your Fortran application using CUDA to harness the massively parallel power of NVIDIA GPUs.

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