Request a full-day workshop at your location, led by a DLI-certified instructor. You’ll get hands-on training and access to GPUs in the cloud to implement and deploy a project from end to end.
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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In-Person Workshops
Certificate Available
Deep Learning Workshops
DEEP LEARNING FUNDAMENTALS
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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
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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
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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
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Fundamentals of Deep Learning for Multi-GPUs
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
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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
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Deep Learning for Digital Content Creation Using Autoencoders
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
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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
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Deep Learning for Industrial Inspection
Explore how to build a deep learning model to automate the verification of capacitors in NVIDIA's printed circuit board (PCB) using a real production dataset. This can lower the verification cost and increase the production throughput across a variety of manufacturing use cases. You'll learn how to:
- Extract meaningful insights from the provided dataset using Pandas DataFrame and NumPy library
- Apply transfer-learning to a deep learning classification model known as InceptionV3
- Optimize the trained InceptionV3 model on V100 GPU using TensorRT 5
- Experiment with FP16 half-precision fast inferencing using V100’s TensorCore
Upon completion, you'll be able to design, train, test, and deploy building blocks of a hardware-accelerated industrial inspection pipeline.
Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.
Technologies: TensorFlow
- Extract meaningful insights from the provided dataset using Pandas DataFrame and NumPy library
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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: Basic experience with neural networks and Python programming; familiarity with linguistics
Technologies: TensorFlow, Keras
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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: Experience with stochastic gradient descent mechanics, network architecture, and parallel computing
Technologies: TensorFlow
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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
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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
Accelerated Computing Workshops
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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 command line and visual profiling 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 tools and techniques. 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
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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 Computing Workshops
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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