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: 23/10, TBD

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

Workshop dates: 29/10, TBD

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: TBD

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: TBD

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…

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: TBD

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: 22/10, TBD

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: 23/10, TBD

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

October 22nd 2021

Sri Phani | NIT Andhra Pradesh

October 30th 2021

Pratik Shah | IIIT Vadodara

November 12th 2021

Madhushree Basavarajaiah | NVIDIA

Building Transformer-Based Natural Language Processing Applications

October 23rd 2021

Priyanka Sharma | DLI Ambassador

Fundamentals of Accelerated Computing with CUDA C/C++

October 29th 2021

Manish Modani | 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: TBD

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: 29/10, TBD

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