NVIDIA Deep Learning Institute (DLI) is collaborating with NVIDIA DLI Certified Instructors to deliver workshops that are delivered remotely through a virtual classroom FREE of cost for Faculty and Students.
The workshops will be conducted by volunteer DLI Certified Instructors (NVIDIA DLI University Ambassadors)
Workshop dates: Coming Soon
Fundamentals of Deep Learning
Learn how deep learning works through hands-on exercises in CV & NLP over this course…
Building Transformer-Based Natural Language Processing Applications
Explore with hands on lab's, Transformer-based NLP models for text classification tasks like categorizing documents in this workshop…
Fundamentals of Accelerated Computing with CUDA C/C++
This course covers the basic tools and strategies for using CUDA® to accelerate C/C++ applications on massively parallel GPUs…
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…
Fundamentals of Accelerated Data Science
Learn how to build and execute end-to-end GPU-accelerated data science workflows that enable you to quickly explore, iterate, and get your work into production in this workshop…
Building Conversational AI Applications
Learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities using the NVIDIA Riva framework…
Applications of AI for Predictive Maintenance
Predictive maintenance helps detect equipment failure before it happens, reducing costly downtime. You will identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions in this workshop…
Accelerating Data Engineering Pipelines
In this workshop, we’ll explore how to improve data pipelines and how by using advanced data engineering tools and techniques you can get significant performance acceleration to produce fresher dashboards and ML models…
Building Intelligent Recommender Systems
In this workshop, you will build intelligent recommender systems - the secret ingredient that enables personalized experiences and create powerful decision support tools across industry applications…
Applications of AI for Anomaly Detection
In this workshop, you'll learn to train AI models for a specific use case to automatically analyze datasets, define “normal behavior,” & identify breaches in patterns quickly and effectively…
The DLI University Ambassador Program gives qualified educators everything they need to teach DLI workshops to faculty, students, and researchers across campuses and academic conferences—all at no cost.
If you’re a faculty and qualified instructor with expertise in deep learning and accelerated computing and want to be a DLI Certified Instructor and Ambassador, apply today.
Available dates: 28/8
Anomaly Detection helps catch data abnormalities before they impact the business. In this workshop, you'll learn to train AI models for a specific use case to automatically analyze datasets, define “normal behavior,” & identify breaches in patterns quickly and effectively.
You’ll learn three different anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques. At the end of the workshop, you’ll be able to use AI to detect anomalies in your work across telecommunications, cybersecurity, finance, manufacturing, and other key industries.
By participating in this workshop, you’ll:
Prerequisites:
Technologies: NVIDIA RAPIDS™, XGBoost, TensorFlow, Keras, pandas, autoencoders, GANs
Available dates: Coming Soon
In this workshop, you will build intelligent recommender systems - the secret ingredient that enables personalized experiences and create powerful decision support tools across industry applications in healthcare, retail, finance and other industries.. Also, learn how to accelerate the solution for real time recommendations based on AI modeling on large datasets. Further, build a container to deploy the model on an inferencing server.
Technologies: CuDF, CuPy, TensorFlow 2, NVIDIA Triton™ Inference Server
In this workshop, we’ll explore how to improve data pipelines and how by using advanced data engineering tools and techniques you can get significant performance acceleration to produce fresher dashboards and ML models, so that users can have the most current information at their fingertips.
Data engineering is the foundation of data science and lays the groundwork for analysis and modeling. In order for organizations to extract knowledge and insights from structured and unstructured data, fast access to accurate and complete datasets is critical. Working with massive amounts of data from disparate sources requires complex infrastructure and expertise. Minor inefficiencies can result in major costs, both in terms of time and money, when scaled across millions to trillions of data points.
Technologies: pandas, cuDF, Dask, NVTabular, Plotly
Predictive maintenance helps detect equipment failure before it happens, reducing costly downtime. You will identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions in this workshop. Learn how to prepare time-series data for AI model training, develop an XGBoost ensemble tree model, build a deep learning model using an LSTM network, and create an autoencoder that detects anomalies for predictive maintenance. At the end of the workshop, you’ll be able to use AI to estimate the condition of equipment and predict when maintenance should be performed.
Technologies: Python, TensorFlow, Keras, XGBoost, NVIDIA RAPIDS™, cuDF, LSTM, autoencoders,
Learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities using the NVIDIA Riva framework. Riva provides a complete, GPU-accelerated software stack, making it easy to quickly create, deploy, and run end-to-end, real-time customised conversational AI applications. The Riva framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks with real-time transcription and natural language processing (NLP) capabilities. You’ll integrate NVIDIA Riva automatic speech recognition (ASR) and named entity recognition (NER) models with a web-based application to produce transcriptions of audio inputs with highlighted relevant text. You'll then customize the NER model, using NVIDIA TAO Toolkit to provide different targeted highlights for the application. Finally, you'll explore the production-level deployment performance and scaling considerations of Riva services.
Technologies: NVIDIA Riva, NVIDIA TAO Toolkit, Kubernetes
Available dates: 18/8
Learn how to build and execute end-to-end GPU-accelerated data science workflows that enable you to quickly explore, iterate, and get your work into production in this workshop.Using the RAPIDS™-accelerated data science libraries, you’ll apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.
Prerequisites: Experience with Python, ideally including pandas and NumPy;Suggested resources to satisfy. Kaggle's pandas Tutorials, Kaggle's Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS
Technologies: RAPIDS, cuDF, XGBoost, cuML, cuGraph, Dask, cuPy, pandas, NumPy, Bokeh
Available dates: 4/8, 2/9, 30/9
Learn how deep learning works through hands-on exercises in CV & NLP over this course. In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. In this workshop, you’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.
Prerequisites: An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.
Technologies: Tensorflow 2 with Keras, Pandas.
Certificate: Yes.
Available dates: 6/8, 9/9
Explore with hands on lab's, Transformer-based NLP models for text classification tasks like categorizing documents in this workshop. You’ll also learn how to leverage 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.
Suggested materials to satisfy prerequisites: Python Tutorial, Overview of Deep Learning Frameworks, PyTorch Tutorial, Deep Learning in a Nutshell, Deep Learning Demystified
Technologies: PyTorch, pandas, NVIDIA NeMo™, NVIDIA Triton™ Inference Server
Note
Available dates: 19/8, 3/9
Learn how to build and execute end-to-end GPU-accelerated data science workflows that enable you to quickly explore, iterate, and get your work into production in this workshop. Using the RAPIDS™-accelerated data science libraries, you’ll apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.
Prerequisites: Experience with Python, ideally including pandas and NumPy
Suggested resources to satisfy prerequisites: Kaggle's pandas Tutorials, Kaggle's Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS
Available dates: 20/8
This course covers the basic tools and strategies for using CUDA® to accelerate C/C++ applications on massively parallel GPUs. You'll learn how to develop code, use CUDA to parallelize it, optimise memory migration between the CPU and GPU accelerator, and apply what you've learned to a new task: accelerating a fully functional but CPU-only particle simulator for observable significant speed benefits. You'll get access to additional materials at the end of the workshop, allowing you to construct new GPU-accelerated applications on your own.
At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerating C/C++ applications with CUDA and be able to:
Prerequisites: Basic C/C++ competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations. No previous knowledge of CUDA programming is assumed.
Tools, Libraries, and Frameworks Used CUDA C++, nvcc, Nsight Systems