NVIDIA DLI AMBASSADOR WORKSHOP SERIES

Learn how deep learning works through hands-on exercises in CV & NLP over this course…

Building Transformer-Based Natural Language Processing Applications

Workshop dates: 17/6

Explore with hands on lab's, Transformer-based NLP models for text classification tasks like categorizing documents in this workshop…

Workshop dates: 3/6

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…

Workshop dates: 27/5

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

Workshop dates: 26/5, 24/6

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

Workshop dates: 9/7

Applications of AI for Predictive Maintenance

Workshop dates: 1/7

Accelerating Data Engineering Pipelines

Workshop dates: 8/7

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

Workshop dates: 15/7

Applications of AI for Anomaly Detection

Workshop dates: 23/7

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…

JOIN THE NVIDIA DLI UNIVERSITY AMBASSADOR PROGRAM

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.

APPLY

Available dates: 23/7

Applications of AI for Anomaly Detection

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:

Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs
Detect anomalies in datasets with both labeled and unlabeled data
Classify anomalies into multiple categories regardless of whether the original data was labeled

Prerequisites:

Professional data science experience using Python
Experience training deep neural networks

Technologies:
NVIDIA RAPIDS™, XGBoost, TensorFlow, Keras, pandas, autoencoders, GANs

Available dates: 15/7

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 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.

By participating in this workshop, you’ll:

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 and NVTabular data loaders 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.

Prerequisites:

Intermediate knowledge of Python, including understanding of list comprehension
Data science experience using Python
Familiarity with NumPy and matrix mathematics

Technologies:
CuDF, CuPy, TensorFlow 2, NVIDIA Triton™ Inference Server

Available dates: 8/7

Accelerating Data Engineering Pipelines

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.

By participating in this workshop, you’ll:

How data moves within a computer. How to build the right balance between CPU, DRAM, Disk Memory, and GPUs.
How different file formats can be read and manipulated by hardware.
How to scale an ETL pipeline with multiple GPUs using NVTabular.
How to build an interactive Plotly dashboard where users can filter on millions of data points in less than a second.

Prerequisites:

Intermediate knowledge of Python (list comprehension, objects)
Familiarity with pandas a plus
Introductory statistics (mean, median, mode)

Technologies:
pandas, cuDF, Dask, NVTabular, Plotly

Available dates: 1/7

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. 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.

By participating in this workshop, you’ll:

Use AI-based predictive maintenance to prevent failures and unplanned downtimes
Identify key challenges around detecting anomalies that can lead to costly breakdowns
Use time-series data to predict outcomes with XGBoost-based machine learning classification models
Use an LSTM-based model to predict equipment failure
Use anomaly detection with time-series autoencoders to predict failures when limited failure-example data is available

Prerequisites:

Experience with Python
Basic understanding of data processing and deep learning

Technologies:
Python, TensorFlow, Keras, XGBoost, NVIDIA RAPIDS™, cuDF, LSTM, autoencoders,

Available dates: 9/7

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. 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.

By participating in this workshop, you’ll:

How to deploy and enable pretrained automatic speech recognition (ASR) and named entity recognition (NER) models on Riva for a conversational AI application
How to fine-tune and deploy domain-specific models with TAO Toolkit
How to deploy a production-level conversational AI application with a Helm Chart for scaling in Kubernetes clusters.

Prerequisites:

Basic Python programming experience
Fundamental understanding of a deep learning framework, such as TensorFlow, PyTorch, or Keras
Basic understanding of neural networks

Technologies:
NVIDIA Riva, NVIDIA TAO Toolkit, Kubernetes

Available dates: 27/5

Fundamentals of Accelerated Computing with CUDA Python

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.

By participating in this workshop, you’ll:

Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
Rapidly achieve massive-scale graph analytics using cuGraph routines

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: 28/5, 4/6, 25/6, 16/7

Fundamentals of Deep Learning

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.

By participating in this 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
Leverage 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.

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: 17/6

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. 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.

By participating in this workshop, you’ll:

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
Leverage 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

Prerequisites:

Experience with Python coding and use of library functions and parameters
Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras
Basic understanding of neural networks

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

Please select the required Workshop and respective Date and register.

Workshop
Name

Workshop
Date

Course
Instructor

Registration
Link

Fundamentals of Accelerated Data Science

MAY 26th 2022

Mitra Bhanu Rath | NVIDIA

Fundamentals of Accelerated Computing with CUDA Python

MAY 27th 2022

Tanmaya Karmarkar | NVIDIA

MAY 28th 2022

Dr. Meenakshi Arya | MITWPU Pune

Fundamentals of Accelerated Computing with CUDA C/C++

JUNE 3rd 2022

Dr. Manish Modani | NVIDIA

Building Transformer-Based Natural Language Processing Applications

JUNE 4th 2022

Romin Chittettu | NVIDIA

JUNE 17th 2022

Megh Makwana | NVIDIA

Fundamentals of Accelerated Data Science

JUNE 24th 2022

Romin Chittettu | NVIDIA

JUNE 25th 2022

Dr. Meenakshi Arya | MITWPU Pune

Applications of AI for Predictive Maintenance

JULY 1st 2022

Sunil Patel | NVIDIA

Accelerating Data Engineering workflows with RAPIDS

JULY 8th 2022

Saurav Agarwal | NVIDIA

Building Conversational AI Applications

JULY 9th 2022

Bharat Giddwani | NVIDIA

Building Intelligent Recommender Systems

JULY 15th 2022

Pallab Maji | NVIDIA

JULY 16th 2022

TBA

Applications of AI for Anomaly Detection

JULY 16th 2022

Amit Kumar | NVIDIA

Note

Register for each workshop separately. Please choose only one date per workshop.
Please prepare for the workshop by creating an NVIDIA account, testing your laptop, and carefully reviewing the course datasheet to become familiar with Prerequisites, Learning Objectives, and Agenda Outline. Refer details at https://developer.nvidia.com/dli/getready and follow instructions there in.
NVIDIA DLI Ambassador Workshop is being offered FREE of cost ($500 value per WS) for Faculty & Students.The registrations are accepted based on details submitted with preferenace for regsitrations with Institute email ID's. There are limited seats for each workshop and these are allottted on First cum First served basis.

Available dates: 26/5, 24/6

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. 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.

By participating in this workshop, you’ll:

Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
Rapidly achieve massive-scale graph analytics using cuGraph routines

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

Technologies:
RAPIDS, cuDF, XGBoost, cuML, cuGraph, Dask, cuPy, pandas, NumPy, Bokeh

Available dates: 3/6

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. 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:

Write code to be executed by a GPU accelerator
Expose and express data and instruction-level parallelism in C/C++ applications using CUDA
Utilize CUDA-managed memory and optimize memory migration using asynchronous prefetching
Leverage command-line and visual profilers to guide your work
Utilize concurrent streams for instruction-level parallelism
Write GPU-accelerated CUDA C/C++ applications, or refactor existing CPU-only applications, using a profile-driven approach

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