Hands on Natural Language Generation and GPT

This training will focus on how the GPT family of models are used for NLP tasks including abstractive text summarization and natural language generation. The training will begin with an introduction to necessary concepts including masked self attention, language models, and transformers and then build on those concepts to introduce the GPT architecture. We will then move into how GPT is used for multiple natural language processing tasks with hands-on examples of using pre-trained GPT-2 models as well as fine-tuning these models on custom corpora. We will also use modern GPT models like ChatGPT and GPT-3 to see how to engineer prompts effectively and efficiently for use in production.

GPT models are some of the most relevant NLP architectures today and it is closely related to other important NLP deep learning models like BERT. 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. (Note that at any given time, only a subset of these classes will be scheduled and open for registration.)