Course Overview
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 cancelled and no refund will be issued, regardless of attendance.
Certifications
This course is part of the following Certifications:
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
Course Objectives
- 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
Course Content
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.
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