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 mini courses to learn a specific technology or technique.

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Fundamentals of Deep Learning for Computer Vision

Prerequisites: Technical background

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 C/C++ competency

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 Python competency

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 C/C++ competency

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

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Deep Learning for Healthcare Image Analysis

Prerequisites: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

Learn how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations.

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Deep Learning for Healthcare Genomics

Prerequisites: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

Learn the basics of deep learning and how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences.

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Image Classification with DIGITS

Prerequisites: None

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

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: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

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: None

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

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Applications of Deep Learning with Caffe, Theano, and Torch

Prerequisites: No technical background required

Explore how deep learning works and how it will change the future of computing.

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

Prerequisites: Bash terminal familiarity

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

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Image Classification with Microsoft Cognitive Toolkit

Prerequisites: None

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

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Linear Classification with TensorFlow

Prerequisites: None

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

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Signal Processing Using DIGITS

Prerequisites: None

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

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Object Detection for Full Motion Video

Prerequisites: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

Discover how to analyze video data by implementing object detection methods using deep learning.

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Object Tracking for Large Scale Full Motion Video

Prerequisites: “Object Detection for Full Motion Video”

Explore how to track moving objects in large-scale video datasets using a deep neural network model.

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Deployment for Intelligent Video Analytics using TensorRT

Prerequisites: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

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

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Image Creation using GANs with TensorFlow and DIGITS

Prerequisites: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

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: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

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: “Fundamentals of Deep Learning for Computer Vision” or similar deep learning experience

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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Accelerating Applications with CUDA C/C++

Prerequisites: Basic C/C++ competency

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

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GPU Memory Optimizations with CUDA C/C++

Prerequisites: Basic CUDA C/C++ competency

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: Basic CUDA C/C++ competency

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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Using Thrust to Accelerate C++

Prerequisites: Basic CUDA C/C++ competency

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

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Accelerating Applications with GPU-Accelerated Libraries in Python

Prerequisites: None

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

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Accelerating Applications with CUDA Fortran

Prerequisites: None

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

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 in Fortran

Prerequisites: Basic CUDA Fortran Competency

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

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Introduction to Accelerated Computing

Prerequisites: None

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

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Profile-Driven Approach to Accelerate Seismic Application with OpenACC

Prerequisites: None

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

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