NVIDIA Deep Learning Institute

                  Training You to Solve the World’s Most Challenging Problems

                  欧美黄色

                  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, instructor-led workshops for teams, and downloadable course materials for university educators.

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                    Online
                    Courses

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                    Instructor-Led
                    Workshops

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                    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 

                    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

                    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

                  • Optimization and Deployment of TensorFlow Models with TensorRT

                    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

                    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 experience training deep learning models in Python

                    Technologies: Horovod, TensorFlow, Keras, Python

                    Duration: 2 hours

                    Price: $30 (excludes tax, if applicable)

                  • Getting Started with Image Segmentation

                    Learn how to categorize segments of an image.

                    Prerequisites: Basic experience training neural networks

                    Technologies: TensorFlow

                    Duration: 2 hours

                    Price: $30 (excludes tax, if applicable)

                  • Modeling Time Series Data with Recurrent Neural Networks in Keras

                    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)

                  DEEP LEARNING FOR HEALTHCARE

                  • Medical Image Classification Using the MedNIST Dataset

                    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)

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

                    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)

                  • Coarse-to-Fine Contextual Memory for Medical Imaging

                    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)

                  • Data Augmentation and Segmentation with Generative Networks for Medical Imaging

                    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)

                  DEEP LEARNING FOR INTELLIGENT VIDEO ANALYTICS

                  • AI Workflows for Intelligent Video Analytics with DeepStream

                    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

                    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++ 

                    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

                    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

                    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

                    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)

                  • OpenACC – 2X in 4 Steps

                    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)

                  ACCELERATED DATA SCIENCE COURSES

                  • Fundamentals of Accelerated Data Science with RAPIDS

                    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

                    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, cuDF, cuML, XGBoost

                    Duration: 2 hours

                    Price: $30 (excludes tax, if applicable)

                  AI COURSES FOR IT

                  • Introduction to AI in the Data Center

                    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.

                  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, we recommend full-day workshops led by DLI-certified instructors. You can request a full-day workshop onsite or remote delivery for your team. 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.

                  Certificate Available

                  Deep Learning Workshops

                  DEEP LEARNING FUNDAMENTALS

                  • Fundamentals of Deep Learning for Multi-GPUs 

                    Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently.

                    In this course, you will learn how to scale deep learning training to multiple GPUs. 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 course will teach you how to use multiple GPUs to train 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 Horovod.

                    Prerequisites: Competency in the Python programming language and experience training deep learning models in Python

                    Technologies: Python, Tensorflow

                  • Fundamentals of Deep Learning for Computer Vision 

                    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 

                    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 

                    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 BY INDUSTRY

                  • Deep Learning for Autonomous Vehicles—Perception

                    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 Robotics

                    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 familiarity with deep neural networks, basic coding experience in Python or similar language

                  • Applications of AI for Anomaly Detection

                    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

                    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

                  • Deep Learning for Industrial Inspection

                    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

                    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 the DeepStream SDK

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

                    Technologies: deep learning, intelligent video analytics, deepstream 3.0, tensorflow, iva, fmv, opencv, accelerated video decoding/encoding, object detection and tracking, anomaly detection, deployment, optimization, data preparation

                  • Deep Learning for Healthcare Image Analysis

                    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

                  Accelerated Computing Workshops

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

                    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

                    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

                    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

                  NETWORKING WORKSHOPS

                  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.

                  PUBLIC WORKSHOPS

                  If you would like to receive updates on upcoming DLI public workshops, sign up to receive communications.

                  CUMULUS BOOTCAMPS

                  Training by NVIDIA Partners

                  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

                  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

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

                  Arizona State University
                  Columbia
                  The Hong Kong University Of Science And Technology
                  Massachusetts Institute of Technology
                  NUS - National University of Singapore
                  University of Oxford
                  Arizona State University
                  Columbia
                  The Hong Kong University Of Science And Technology
                  Massachusetts Institute of Technology
                  NUS - National University of Singapore
                  University of Oxford
                  NVIDIA GTC

                  Partners

                  DLI works with industry partners to build DLI content and deliver DLI instructor-led workshops around the world. Here are some of our leading partners.