Building Transformer-Based Natural Language Processing Applications
Data Science Hub / NVIDIA Workshop
Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized natural language processing (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 and named-entity recognition (NER) tasks. You’ll also learn 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.
When: Oct 24-25, 8:30 am - 12:30 pm (CT).
Where: This workshop takes place virtually via Zoom. Registrants will receive the Zoom link in an email one week prior to the workshop.
- 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
- 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
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.
Agenda: To review the full workshop agenda, please visit https://www.nvidia.com/en-us/training/instructor-led-workshops/natural-language-processing/.
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.