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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, accelerated computing, and accelerated data science. 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. Get started with DLI through self-paced, online training for individuals, in-person workshops for teams, and downloadable course materials for university educators.

Online
Courses
In-Person
Workshops
University
Training

For self-learners and small teams, we recommend self-paced, online training through DLI and online courses through our partners. With DLI, you’ll have access to a fully configured, GPU-accelerated server in the cloud, gain practical skills for your work, and have the opportunity to earn a certificate of subject matter competency.

Online training with DLI

Certificate Available

Deep Learning Courses

DEEP LEARNING FUNDAMENTALS

Fundamentals of Deep Learning for Computer Vision

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

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

Technologies: Caffe, DIGITS

Duration: 8 hours

Price: $90 (excludes tax, if applicable)
Getting Started with AI on Jetson Nano

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Explore how to build a deep learning classification project with computer vision models using an NVIDIA^? Jetson^? Nano Developer Kit.

Prerequisites: Familiarity with Python (helpful, not required)

Technologies: PyTorch, Jetson Nano

Duration: 8 hours

Price: Free
Image Classification with DIGITS

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

Prerequisites: None

Technologies: Caffe (with DIGITS interface)

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Object Detection with DIGITS

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

Prerequisites: Basic experience with neural networks

Technologies: Caffe (with DIGITS interface)

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Optimization and Deployment of TensorFlow Models with TensorRT

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Learn how to optimize TensorFlow models to generate fast inference engines in the deployment stage.

Prerequisites: Experience with TensorFlow and Python

Technologies: TensorFlow, Python, NVIDIA TensorRT^? (TF-TRT)

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Deep Learning at Scale with Horovod

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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.

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

Technologies: Horovod, TensorFlow, Keras, Python

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Image Segmentation with TensorFlow

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

Prerequisites: Basic experience training neural networks

Technologies: TensorFlow

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Signal Processing with DIGITS

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Learn how to classify both image and image-like data using deep learning by converting radio frequency (RF) signals into images to detect a weak signal corrupted by noise.

Prerequisites: Basic experience training neural networks

Technologies: Caffe, DIGITS

Duration: 2 hours

Price: $30 (excludes tax, if applicable)

DEEP LEARNING FOR DIGITAL CONTENT CREATION

Image Style Transfer with Torch

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

Prerequisites: Experience with CNNs

Technologies: Torch, CNNs

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Rendered Image Denoising Using Autoencoders

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Explore how neural networks with autoencoders can be used to dramatically speed up the removal of noise in ray-traced images.

Prerequisites: Experience with CNNs

Technologies: TensorFlow, CNNs

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Image Super Resolution Using Autoencoders

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

Prerequisites: Experience with CNNs

Technologies: Keras, CNNs

Duration: 2 hours

Price: $30 (excludes tax, if applicable)

DEEP LEARNING FOR HEALTHCARE

Modeling Time Series Data with Recurrent Neural Networks in Keras

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Explore how to classify and forecast time-series data, such as modeling a patient's health over time, using recurrent neural networks (RNNs).

Prerequisites: Basic experience with deep learning

Technologies: Keras

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Medical Image Classification Using the MedNIST Dataset

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Explore an introduction to deep learning for radiology and medical imaging by applying CNNs to classify images in a medical imaging dataset.

Prerequisites: Basic experience with Python

Technologies: PyTorch, Python

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Data Science Workflows for Deep Learning in Medical Applications

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Learn how to apply data augmentation and standardization techniques on a medical imaging dataset and validate your techniques by training a CNN on the dataset.

Prerequisites: Basic experience with Python and CNNs

Technologies: PyTorch, Python, CNNs

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Medical Image Segmentation with DIGITS

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Explore how to segment MRI images to measure parts of the heart using deep learning techniques.

Prerequisites: Basic experience with CNNs and Python

Technologies: DIGITS, Caffe, CNNs, Python

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Image Classification with TensorFlow: Radiomics—1p19q Chromosome Status Classification

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Learn how to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.

Prerequisites: Basic experience with CNNs and Python

Technologies: TensorFlow, CNNs, Python

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Medical Image Analysis with R and MXNet

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Explore how to use CNNs for medical image analysis to infer patient status from non-visible images.

Prerequisites: Basic experience with CNNs and Python

Technologies: MXNet, CNNs, Python

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Data Augmentation and Segmentation with Generative Networks for Medical Imaging

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Learn how to use generative adversarial networks (GANs) for medical imaging by applying them to the creation and segmentation of brain MRIs.

Prerequisites: Experience with CNNs

Technologies: TensorFlow, GANs, CNNs

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Coarse-to-Fine Contextual Memory for Medical Imaging

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Learn how to use Coarse-to-Fine Context Memory (CFCM) to improve traditional architectures for medical image segmentation and classification tasks.

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

Technologies: TensorFlow, CNNs, CFCM

Duration: 2 hours

Price: $30 (excludes tax, if applicable)

DEEP LEARNING FOR INTELLIGENT VIDEO ANALYTICS

AI Workflows for Intelligent Video Analytics with DeepStream

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Learn how to build hardware-accelerated applications for intelligent video analytics (IVA) with DeepStream and deploy them at scale to transform video streams into insights.

Prerequisites: Experience with C++ and Gstreamer

Technologies: DeepStream3, C++, Gstreamer

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Getting Started with DeepStream for Video Analytics on Jetson Nano

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Learn how to build DeepStream applications to annotate video streams using object detection and classification networks.

Prerequisites: Basic familiarity with C

Technologies: DeepStream, TensorRT, Jetson Nano

Duration: 8 hours; Self-paced

Price: Free

Accelerated Computing Courses

Fundamentals of Accelerated Computing with CUDA C/C++

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Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniques and the Nsight Systems profiler.

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

Technologies: C/C++, CUDA

Duration: 8 hours

Price: $90 (excludes tax, if applicable)
Fundamentals of Accelerated Computing with CUDA Python

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Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on GPUs.

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.

Technologies: CUDA, Python, Numba, NumPy

Duration: 8 hours

Price: $90 (excludes tax, if applicable)
Fundamentals of Accelerated Computing with OpenACC

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Explore how to build and optimize accelerated heterogeneous applications on multiple GPU clusters using OpenACC, a high-level GPU programming language.

Prerequisites: Basic experience with C/C++

Technologies: OpenACC, C/C++

Duration: 8 hours

Languages: English

Price: $90 (excludes tax, if applicable)
High-Performance Computing with Containers

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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.

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

Technologies: Docker, Singularity, HPCCM, C/C++

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Accelerating Applications with CUDA C/C++

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

Prerequisites: Basic experience with C/C++

Technologies: C/C++, CUDA

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
OpenACC – 2X in 4 Steps

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

Prerequisites: Basic experience with C/C++

Technologies: C/C++, OpenACC

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
GPU Memory Optimizations with CUDA C/C++

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Learn memory optimization techniques for programming with CUDA C/C++ on a GPU and how to use the NVIDIA Visual Profiler (NVVP) to support these optimizations.

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

Technologies: CUDA C/C++, NVVP

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Accelerating Applications with GPU-Accelerated Libraries in C/C++

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

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

Technologies: C/C++, CUDA

Duration: 2 hours

Price: $30 (excludes tax, if applicable)
Using Thrust to Accelerate C++

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

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

Technologies: C/C++, Thrust

Duration: 2 hours

Price: $30 (excludes tax, if applicable)

ACCELERATED DATA SCIENCE COURSES

Fundamentals of Accelerated Data Science with RAPIDS

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Learn how to perform multiple analysis tasks on large datasets using RAPIDS, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.

Prerequisites: Experience with Python, including pandas and NumPy

Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python

Duration: 6 hours

Price: $90 (excludes tax, if applicable)
Accelerating Data Science Workflows with RAPIDS

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Learn to build a GPU-accelerated, end-to-end data science workflow using RAPIDS open-source libraries for massive performance gains.

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

Technologies: RAPIDS, Pandas, NumPy, scikit-learn

Duration: 2 hours

Price: $30 (excludes tax, if applicable)

AI COURSES FOR IT

Introduction to AI in the Data Center

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Explore an introduction to AI, GPU computing, NVIDIA AI software architecture, and how to implement and scale AI workloads in the data center. You'll understand how AI is transforming society and how to deploy GPU computing to the data center to facilitate this transformation.

Prerequisites: Basic knowledge of enterprise networking, storage, and data center operations

Technologies: Artificial intelligence, machine learning, deep learning, GPU hardware and software

Duration: 4 hours

Price: $30 (excludes tax, if applicable)

Online Training with Partners

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

Nanodegree: Coursera Deep Learning Specialization

Coursera Deep Learning Specialization

UPCOMING INSTRUCTOR-LED WORKSHOPS

DLI offers public instructor-led workshops around the world at conferences and universities. View the schedule below to find a workshop near you.

For large teams or self-learners interested in training in-person, we recommend full-day workshops led by DLI-certified instructors. You can request a full-day workshop onsite for your team or attend a full-day workshop at NVIDIA GPU Technology Conferences (GTCs) around the world. With DLI, you’ll have access to a fully configured, GPU-accelerated server in the cloud, gain practical skills for your work, and have the opportunity to earn a certificate of subject matter competency.

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Certificate Available

Deep Learning Workshops

DEEP LEARNING FUNDAMENTALS

Fundamentals of Deep Learning for Computer Vision
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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: Familiarity with basic programming fundamentals such as functions and variables

Technologies: Caffe, DIGITS
Fundamentals of Deep Learning for Multiple Data Types
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This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

Implementing deep learning workflows like image segmentation and text generation

Comparing and contrasting data types, workflows, and frameworks

Combining computer vision and natural language processing

Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

Technologies: TensorFlow
Fundamentals of Deep Learning for Natural Language Processing
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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 representations 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: Basic experience with neural networks and Python programming; familiarity with linguistics

Technologies: TensorFlow, Keras
Fundamentals of Deep Learning for Multi-GPUs
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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 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 train neural networks. You'll learn:

Approaches to multi-GPUs 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 stochastic gradient descent mechanics, network architecture, and parallel computing

Technologies: TensorFlow

DEEP LEARNING BY INDUSTRY

Deep Learning for Autonomous Vehicles—Perception
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Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE^? development platform.

You'll learn how to:

Work with CUDA^? code, memory management, and GPU acceleration on the NVIDIA DRIVE AGX^? System

Train a semantic segmentation neural network

Optimize, validate, and deploy a trained neural network using NVIDIA^? TensorRT^?

Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.

Prerequisites: Experience with CNNs and C++

Technologies: TensorFlow, TensorRT, Python, CUDA C++, DIGITS
Deep Learning for Digital Content Creation Using Autoencoders
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Explore the latest techniques for designing, training, and deploying neural networks for digital content creation. You’ll learn how to:

Apply the architectural innovations and training techniques used to make arbitrary video style transfer

Train your own denoiser for rendered images

Upscale images with super resolution AI

Upon completion, you’ll be able to start creating digital assets using deep learning approaches.

Prerequisites: Basic familiarity with deep learning concepts such as convolutional neural networks (CNNs); experience with the Python programming language

Technologies: Torch, TensorFlow
Deep Learning for Healthcare Image Analysis
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This workshop 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 low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status

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

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

Technologies: R, MXNet, TensorFlow, Caffe, DIGITS
Deep Learning for Industrial Inspection
Request Workshop

VIEW DATASHEET

This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

Implementing deep learning workflows like image segmentation and text generation

Comparing and contrasting data types, workflows, and frameworks

Combining computer vision and natural language processing

Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

Technologies: TensorFlow
Deep Learning for Intelligent Video Analytics
Request Workshop

VIEW DATASHEET

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 representations 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: Basic experience with neural networks and Python programming; familiarity with linguistics

Technologies: TensorFlow, Keras
Deep Learning for Robotics
Request Workshop

VIEW DATASHEET

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 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 train neural networks. You'll learn:

Approaches to multi-GPUs 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 stochastic gradient descent mechanics, network architecture, and parallel computing

Technologies: TensorFlow
Applications of AI for Anomaly Detection
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The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)

Build and compare supervised learning with unsupervised learning-based solutions

Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

Prerequisites: Experience with CNNs and Python

Technologies: RAPIDS, Keras, GANs, XGBoost
Applications of AI for Predictive Maintenance
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Learn how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and use this information to map anomalies to failure conditions.

You’ll learn how to:

Leverage predictive maintenance to manage failures and avoid costly unplanned downtimes

Identify key challenges around identifying anomalies that can lead to costly breakdowns

Use time-series data to predict outcomes using machine learning classification models with XGBoost

Apply predictive maintenance procedures by using a long short-term memory ( LSTM)-based model to predict device failure

Experiment with autoencoders to detect anomalies by using the time-series sequences from the previous steps

Upon completion, you’ll understand how to use AI to predict the condition of equipment and estimate when maintenance should be performed.

Prerequisites: Experience with Python and deep neural networks

Technologies: TensorFlow, Keras

Accelerated Computing Workshops

Fundamentals of Accelerated Computing with CUDA C/C++
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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 Nsight Systems 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 techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

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

Technologies: C/C++, CUDA
Fundamentals of Accelerated Computing with CUDA Python
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This workshop 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.

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.

Technologies: CUDA, Python, Numba, NumPy

Accelerated Data Science Workshops

Fundamentals of Accelerated Data Science with RAPIDS
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RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. In this training, you'll:

Use cuDF and Dask to ingest and manipulate massive datasets directly on the GPU

Apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH, and cuML, to perform data analysis at massive scale

Perform multiple analysis tasks on massive datasets in an effort to stave off a simulated epidemic outbreak affecting the UK

Upon completion, you'll be able to load, manipulate, and analyze data orders of magnitude faster than before, enabling more iteration cycles and drastically improving productivity.

Prerequisites: Experience with Python, ideally including pandas and NumPy

Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python

ENTERPRISE SOLUTION

If you’re interested in more comprehensive enterprise training, the DLI Enterprise Solution offers a package of training and lectures to meet your organization’s unique needs. From hands-on online and onsite training to executive briefings and enterprise-level reporting, DLI can help your company transform into an AI organization. Contact us to learn more.

Request Solution

DLI at NVIDIA GTC

Join hands-on training at the NVIDIA GPU Technology Conference (GTC) to connect with experts, network with your peers, and learn the latest in AI, accelerated computing, and accelerated data science. Here are upcoming GTCs around the world:

GTC China | Suzhou | December 16-19, 2019
GTC Silicon Valley | San Jose, CA | March 22-26, 2020

NVIDIA DLI offers downloadable course materials for university educators and free self-paced, online training to students through the DLI Teaching Kits. Educators can also get certified to deliver DLI workshops on campus through the University Ambassador Program.

Teaching Kits

DLI Teaching Kits are available to qualified university educators interested in course solutions across deep learning, accelerated computing, and robotics. Educators can integrate lecture materials, hands-on courses, GPU cloud resources, and more into their curriculum.

Enhancing Curricula with NVIDIA Teaching Kits

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University Ambassador Program

The DLI University Ambassador Program certifies qualified educators to deliver hands-on DLI workshops to university faculty, students, and researchers at no cost. Educators are encouraged to download the DLI Teaching Kits to be qualified for participation in the Ambassador Program.

Furthering the Frontiers of Education

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DLI has certified University Ambassadors at hundreds of universities, including:

NVIDIA Deep Learning Institute

OnlineCourses

In-PersonWorkshops

UniversityTraining

Online training with DLI

Deep Learning Courses

DEEP LEARNING FUNDAMENTALS

Fundamentals of Deep Learning for Computer Vision

Getting Started with AI on Jetson Nano

Image Classification with DIGITS

Object Detection with DIGITS

Optimization and Deployment of TensorFlow Models with TensorRT

Deep Learning at Scale with Horovod

Image Segmentation with TensorFlow

Signal Processing with DIGITS

DEEP LEARNING FOR DIGITAL CONTENT CREATION

Image Style Transfer with Torch

Rendered Image Denoising Using Autoencoders

Image Super Resolution Using Autoencoders

DEEP LEARNING FOR HEALTHCARE

Modeling Time Series Data with Recurrent Neural Networks in Keras

Medical Image Classification Using the MedNIST Dataset

Data Science Workflows for Deep Learning in Medical Applications

Medical Image Segmentation with DIGITS

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

Medical Image Analysis with R and MXNet

Data Augmentation and Segmentation with Generative Networks for Medical Imaging

Coarse-to-Fine Contextual Memory for Medical Imaging

DEEP LEARNING FOR INTELLIGENT VIDEO ANALYTICS

AI Workflows for Intelligent Video Analytics with DeepStream

Getting Started with DeepStream for Video Analytics on Jetson Nano

Accelerated Computing Courses

Fundamentals of Accelerated Computing with CUDA C/C++

Fundamentals of Accelerated Computing with CUDA Python

Fundamentals of Accelerated Computing with OpenACC

High-Performance Computing with Containers

Accelerating Applications with CUDA C/C++

OpenACC – 2X in 4 Steps

GPU Memory Optimizations with CUDA C/C++

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

Using Thrust to Accelerate C++

ACCELERATED DATA SCIENCE COURSES

Fundamentals of Accelerated Data Science with RAPIDS

Accelerating Data Science Workflows with RAPIDS

AI COURSES FOR IT

Introduction to AI in the Data Center

Online Training with Partners

UPCOMING INSTRUCTOR-LED WORKSHOPS

Deep Learning Workshops

DEEP LEARNING FUNDAMENTALS

Fundamentals of Deep Learning for Computer Vision

Fundamentals of Deep Learning for Multiple Data Types

Fundamentals of Deep Learning for Natural Language Processing

Fundamentals of Deep Learning for Multi-GPUs

DEEP LEARNING BY INDUSTRY

Deep Learning for Autonomous Vehicles—Perception

Deep Learning for Digital Content Creation Using Autoencoders

Deep Learning for Healthcare Image Analysis

Deep Learning for Industrial Inspection

Deep Learning for Intelligent Video Analytics

Deep Learning for Robotics

Applications of AI for Anomaly Detection

Applications of AI for Predictive Maintenance

Accelerated Computing Workshops

Fundamentals of Accelerated Computing with CUDA C/C++

Fundamentals of Accelerated Computing with CUDA Python

Accelerated Data Science Workshops

Fundamentals of Accelerated Data Science with RAPIDS

ENTERPRISE SOLUTION

DLI at NVIDIA GTC

Teaching Kits

Enhancing Curricula with NVIDIA Teaching Kits

University Ambassador Program

Furthering the Frontiers of Education

Online
Courses

In-Person
Workshops

University
Training