Nvidia Building Transformer-Based Natural Language Processing Applications (BNLPA)

Ziele der Schulung

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.

Zielgruppe Seminar

This course is designed for AI researchers, data scientists, machine learning engineers, and NLP practitioners who want to gain a deep understanding of the transformer architecture and learn how to train, optimize, and deploy their own NLP models. It is especially suitable for professionals in the fields of artificial intelligence, natural language processing (NLP), deep learning, and software development who work with PyTorch, NVIDIA NeMo, and NVIDIA Triton or wish to strengthen their expertise in these areas.

Voraussetzungen

• 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

Seminarinhalt

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.

Hinweise

Partner

Dieses Seminar bieten wir in Kooperation mit unserem Nvidia Learning Partner Fast Lane Institute for Knowledge Transfer GmbH an.

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