Building Transformer-Based Natural Language Processing Applications Training
Course Authors
Building Transformer-Based Natural Language Processing Applications Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
With Building Transformer-Based Natural Language Processing Applications Training, participants will learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks.
In this course, you’ll:
- Construct a Transformer neural network in PyTorch
- Build a named-entity recognition (NER) application with BERT
- Deploy the NER application with ONNX and TensorRT to a Triton inference server
Upon completion, you’ll be proficient in task-agnostic applications of Transformer-based models.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be canceled, and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Building Transformer-Based Natural Language Processing Applications Training with an expert instructor
- Building Transformer-Based Natural Language Processing Applications Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Building Transformer-Based Natural Language Processing Applications – https://www.wiley.com/
- Building Transformer-Based Natural Language Processing Applications – https://www.packtpub.com/
- Building Transformer-Based Natural Language Processing Applications – https://store.logicaloperations.com/
- Building Transformer-Based Natural Language Processing Applications – https://us.artechhouse.com/
- Building Transformer-Based Natural Language Processing Applications Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Building Transformer-Based Natural Language Processing Applications Training course, participants can:
- How transformers are used as the basic building blocks of modern LLMs for NLP applications
- How self-supervision improves upon the transformer architecture in BERT, Megatron, and other LLM variants for superior NLP results
- How to leverage pretrained, modern LLM models to solve multiple NLP tasks such as text classification, named-entity recognition (NER), and question answering
- 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
CUSTOMIZE IT
- We can adapt this Building Transformer-Based Natural Language Processing Applications Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Building Transformer-Based Natural Language Processing Applications course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Building Transformer-Based Natural Language Processing Applications course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Building Transformer-Based Natural Language Processing Applications course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Building Transformer-Based Natural Language Processing Applications Training course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Building Transformer-Based Natural Language Processing Applications Training course are:
- 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
COURSE SYLLABUS
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Introduction to Transformers
- 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 pretrained transformer model.
Self-Supervision, BERT, and Beyond
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.
Inference and Deployment for NLP
- 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
- 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.
Certifications:
This course is part of the following Certifications:
- NVIDIA-Certified Associate: Generative AI LLMs
