This site requires Javascript in order to view all its content. Please enable Javascript in order to access all the functionality of this web site. Here are the instructions how to enable JavaScript in your web browser.

Instructor-Led Workshop
Building Transformer-Based Natural Language Processing Applications

Request a workshop for your organization

Register for public workshops

Applications for natural language processing (NLP) have exploded in the past decade. With the proliferation of AI assistants and organizations infusing their businesses with more interactive human-machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate text-based data is essential. Modern techniques can capture the nuance, context, and sophistication of language, just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more.

Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more.

In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. 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.

Learning Objectives

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

Download workshop datasheet (PDF 71.9 KB)

Workshop Outline

Introduction (15 mins)	Meet the instructor. Create an account at courses.nvidia.com/join
Introduction to Transformers (120 mins)	Explore how the Transformer architecture works in detail: Build the Transformer architecture in PyTorch. Calculate the self-attention matrix. Translate English to German with a pre-trained Transformer model.
Break (60 mins)
Self-Supervision, BERT, and Beyond (120 mins)	Learn how to apply self-supervised Transformer-based models to concrete NLP tasks using NVIDIA NeMo: Build a text classification project to classify abstracts. Build a NER project to identify disease names in text. Improve project accuracy with domain-specific models.
Break (15 mins)
Inference and Deployment for NLP (120 mins)	Learn how to deploy an NLP project for live inference on NVIDIA Triton: Prepare the model for deployment. Optimize the model with NVIDIA^® TensorRT^™. Deploy the model and test it.
Final Review (15 mins)	Review key learnings and answer questions. Complete the assessment and earn a certificate. Take the workshop survey. Learn how to set up your own environment and discuss additional resources and training.

Workshop Details

Duration: 8 hours

Price: $500 for public workshops, contact us for enterprise workshops.

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

Assessment Type:

Skills-based coding assessments evaluate students’ ability to build an NLP task, including a neural module pipeline and training.
Multiple-choice questions evaluate students’ understanding of the NLP concepts presented in the class

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Languages: English, Simplified Chinese

Upcoming Workshops

Central European Time

GTC

Wed, Apr 14, 2021

9:00 a.m.–5:00 p.m.

Register now

Central European Time

GTC

Wed, Apr 14, 2021

9:00 a.m.–5:00 p.m.

Register now

Central European Time

Wed, May 19, 2021

9:00 a.m.–5:00 p.m.

Register now

Pacific Time

Wed, Jun 9, 2021

9:00 a.m.–5:00 p.m.

Register now

Upcoming Public Workshops

North America / Latin America

Thursday, September 23, 2021
9:00 a.m.–5:00 p.m. PDT

If your organization is interested in boosting and developing key skills in AI, accelerated data science, or accelerated computing, you can request instructor-led training from the NVIDIA DLI.

Request a Workshop

Questions?

Contact Us for Questions on
Deep Learning Training

Section

Section

First Name

Last Name

Business Email Address

Organization / University Name

Industry

Job Title

Job Role

Location

City

State

Reason for requesting contact

本人知悉且同意 NVIDIA Corporation 基于调研、活动组织的目的对本人的上述信息的收集和处理，并已经阅读并同意 <a href="https://www.nvidia.cn/about-nvidia/privacy-policy/" target="_blank">NVIDIA 隐私政策</a>。

本人知悉且同意，因调研、活动组织的必须，以及相对应的 NVIDIA 内部管理和系统操作的需要，上述信息会被传输到位于美国的 NVIDIA Corporation 按照符合 <a href="https://www.nvidia.cn/about-nvidia/privacy-policy/" target="_blank">NVIDIA 隐私政策</a>的方式进行存储，您可以通过发送邮件至 <a href="mailto:privacy@nvidia.com">privacy@nvidia.com</a> 进行联系以解决相关问题，实现可适用的数据保护法所规定的权利。

Instructor-Led Workshop
Building Transformer-Based Natural Language Processing Applications

Learning Objectives

Workshop Outline

Workshop Details

Upcoming Workshops

Wed, Apr 14, 2021

Wed, Apr 14, 2021

Wed, May 19, 2021

Wed, Jun 9, 2021

Upcoming Public Workshops

North America / Latin America

Questions?

Contact Us for Questions on
Deep Learning Training

Read our FAQs.

Inquire about NVIDIA Deep Learning Institute services.

For technical questions, check out the NVIDIA Developer Forums.

Instructor-Led Workshop Building Transformer-Based Natural Language Processing Applications

Learning Objectives

Workshop Outline

Workshop Details

Upcoming Workshops

Wed, Apr 14, 2021

Wed, Apr 14, 2021

Wed, May 19, 2021

Wed, Jun 9, 2021

Upcoming Public Workshops

North America / Latin America

Questions?

Contact Us for Questions on Deep Learning Training

Read our FAQs.

Inquire about NVIDIA Deep Learning Institute services.

For technical questions, check out the NVIDIA Developer Forums.

Instructor-Led Workshop
Building Transformer-Based Natural Language Processing Applications

Contact Us for Questions on
Deep Learning Training