Hands-on, Instructor-led Training

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI, accelerated computing, and accelerated data science to help developers, data scientists, and other professionals solve their most challenging problems. These in-depth workshops are taught by experts in their respective fields, delivering industry-leading technical knowledge to drive breakthrough results for individuals and organizations. Complete a full day workshop and earn an NVIDIA DLI certificate to demonstrate subject matter competency and accelerate your career growth.

Instructor-led Remote Workshops

Access GPU-accelerated workstations in the cloud to learn how to train, optimize, and deploy neural networks using the latest deep learning tools, frameworks, and SDKs.  

DLI workshops at GTC were hosted by our sponsor:


Workshop Offerings

Fundamentals of Deep Learning

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn how to use free, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

  • Learn the fundamental techniques and tools required to train a deep learning model
  • Gain experience with common deep learning data types and model architectures
  • Enhance datasets through data augmentation to improve model accuracy
  • Use transfer learning between models to achieve efficient results with less data and computation
  • Build confidence to take on your own project with a modern deep learning framework

Building Intelligent Recommender Systems

Deep learning-based recommender systems are the secret ingredient behind personalized online experiences and powerful decision support tools in retail, entertainment, healthcare, finance, and other industries. This workshop covers the fundamental tools and techniques for building highly effective recommender systems, as well as how to deploy GPU-accelerated solutions for real-time recommendations.

By participating in this is workshop, you’ll learn how to:

  • Build a content-based recommender system using the open-source cuDF library and Apache Arrow
  • Construct a collaborative filtering recommender system using alternating least squares (ALS) and CuPy
  • Design a wide and deep neural network using TensorFlow 2 to create a hybrid recommender system
  • Optimize performance for both training and inference using large, sparse datasets
  • Deploy a recommender model as a high-performance web service

Building Transformer-Based Natural Language Processing Applications

Discover how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You’lll learn how to use Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

  • Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
  • See how Transformer architecture features—especially self-attention—are used to create language models without RNNs
  • Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results 
  • Take advantage of pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
  • Manage inference challenges and deploy refined models for live applications

Fundamentals of Accelerated Computing with CUDA Python

This course 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 NVIDIA(R) CUDA(R) 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.

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 X-GBoost
  • 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.

Fundamentals of Accelerated Data Science with RAPIDS

NVIDIA RAPIDS™ is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.

In this training, you will: 

  • 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 fast, enabling more iteration cycles and drastically improving productivity.  

Want more training?

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