Building Transformer-Based Natural Language Processing Applications

About this course

Understand the transformer architecture behind modern language models and put it to work. You will explore self-supervision in BERT and its successors, then apply pre-trained models to text classification, named-entity recognition and question answering, and deploy them for inference.

What you'll learn

• 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

Curriculum

Who it's for

Data scientists and developers with Python and basic deep learning knowledge.

Before you start

• 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