Hands on Transfer Learning for NLP with BERT

This training will focus on implementing BERT to solve a variety of modern NLP tasks including information retrieval, sequence classification / regression, and question/answering tasks. The training will begin with a refresher on the BERT architecture and how BERT learns to model language. We will then move into examples of fine-tuning BERT on domain-specific corpora and using pre-trained models to perform NLP tasks out of the box.

BERT is one of the most relevant NLP architectures today and it is closely related to other important NLP deep learning models like GPT-3. Both of these models are derived from the newly invented transformer architecture and represent an inflection point in how machines process language and context.

The Natural Language Processing with Next-Generation Transformer Architectures series of online trainings provides a comprehensive overview of state-of-the-art natural language processing (NLP) models including GPT and BERT which are derived from the modern attention-driven transformer architecture and the applications these models are used to solve today. All of the trainings in the series blend theory and application through the combination of visual mathematical explanations, straightforward applicable Python examples within hands-on Jupyter notebook demos, and comprehensive case studies featuring modern problems solvable by NLP models. This training is part of a series and assumes that the attendee is coming in with knowledge from BERT Transformer Architecture for NLP training. (Note that at any given time, only a subset of these classes will be scheduled and open for registration.)