"> Hands-On,Instructor Led Online Training on AI | NVIDIA GTC
  • Hands-on, Instructor-led Training

    The NVIDIA Deep Learning Institute (DLI) offers instructor-led training on AI, accelerated computing, and accelerated data science. Interested parties can choose from all-day workshops or two-hour training sessions.

    These courses are live, online, and only available in English. Because training sessions are taught by a live instructor and we want to ensure proper teacher-to-student ratios, capacity is limited. Classes fill up quickly so register and sign up early to reserve your spot.

    Training sessions will not be recorded or made available for later viewing.

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    Instructor-led Online Workshops

    Get access to a GPU-accelerated server in the cloud to complete hands-on exercises and earn a certificate in AI or accelerated computing.
    To add a workshop to your package, log in to your GTC account, then click on ADD PACKAGE to select and pay.
    GTC Digital Special Rate: $79

    Fundamentals of Accelerated Computing with CUDA C/C++

    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.
    Technologies: C/C++, CUDA
    Date & Time: March 26, 9:00 - 17:00 PT

    The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs.

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    Fundamentals of Accelerated Data Science with RAPIDS

    Prerequisites: Experience with Python, ideally including pandas and NumPy
    Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python
    Date & Time: March 25, 9:00 - 17:00 PT

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

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    Fundamentals of Accelerated Data Science with RAPIDS
    Applications of AI for Predictive Maintenance

    Applications of AI for Predictive Maintenance

    Prerequisites: Experience with CNNs and C++
    Technologies: TensorFlow, Keras
    Date & Time: April 1, 9:00 - 17:00 PT

    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.  

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    Fundamentals of Deep Learning for Multi-Gpus - [sold out]

    Prerequisites: Experience with stochastic gradient descent mechanics, network architecture, and parallel computing
    Technologies:
    TensorFlow
    Date & Time: March 30, 9:00 - 17:00 PT

    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.

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    Fundamentals of Deep Learning for Multi-Gpus
    Applications of AI for Anomaly Detection

    Applications of AI for Anomaly Detection

    Prerequisites: Experience with CNNs and Python
    Technologies: RAPIDS, Keras, GANs, XGBoost
    Date & Time: March 31, 9:00 - 17:00 PT

    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.

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    Fundamentals of Deep Learning with Multiple Data Types

    Prerequisites: Familiarity with basic Python (functions and variables), prior experience training neural networks.
    Technologies: TensorFlow and TensorBoards
    Date & Time: March 27, 9:00 - 17:00 PT

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

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    Deep Learning for Autonomous Vehicles - Perception
    Applications of AI for Anomaly Detection

    Deep Learning for Autonomous Vehicles – Perception - [Sold Out]

    Prerequisites: Experience with CNNs and C++
    Technologies: TensorFlow, TensorRT, Python, CUDA C++, DIGITS
    Date & Time: April 2, 9:00 - 17:00 PT

    Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE? development platform.

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    Deep Learning Institute Trainings

    Instructor-led training sessions are $39 each.

    Deep Learning Institute Trainings

    2-hour online training sessions are $39 each. See all available online trainings here.

    DEEP LEARNING INSTITUTE TRAININGS DETAILS
    Introduction to CUDA Python with Numba
    April 4, 9 – 10:30
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    Accelerating Data Science Workflows with RAPIDS V2
    April 4, 11 – 12:30
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    Integrating Custom Sensors with DriveWorks CORE
    April 4, 13 – 14:30
    Learn more >
    FUNDAMENTALS OF DEEP LEARNING FOR MULTI-GPUS
     
    $79
    APPLICATIONS OF AI FOR ANOMALY DETECTION
     
    $79
    FUNDAMENTALS OF DEEP LEARNING WITH MULTIPLE DATA TYPES
     
    $79
    DEEP LEARNING FOR AUTONOMOUS VEHICLES – PERCEPTION
     
    $79

    WANT MORE TRAINING?

    The NVIDIA Deep Learning Institute offers self-paced, online training powered by GPU-accelerated workstations in the cloud.

    Optimization and Deployment of TensorFlow Models with TensorRT

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

    Introduction to CUDA Python with Numba

    Explore how to use Numba to accelerate NumPy ufuncs in your Python code and write custom CUDA kernels in Python.

    Deep Autoencoders for Recommendation Systems

    Learn how to build recommendation systems for your customers using deep autoencoders.