Applications for natural language processing (NLP) and generative AI have exploded in the past decade.

With the proliferation of applications like chatbots and intelligent virtual assistants, organizations are infusing their businesses with more interactive human-machine experiences. Understanding how Transformer-based large language models (LLMs) can be used to manipulate, analyze, and generate text-based data is essential.

Modern pretrained LLMs can encapsulate the nuance, context, and sophistication of language, just as humans do. When fine-tuned and deployed correctly, developers can use these LLMs to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more.

Transformer-based LLMs, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines. This is accomplished on benchmarks like SQuAD for question answering, entity recognition, intent recognition, sentiment analysis, and more.

In this workshop, you’ll learn how Transformers are used as the building blocks of modern large language models (LLMs). You’ll then use these models for various NLP tasks, including text classification, named-entity recognition (NER), author attribution, and question answering. 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.

In this course, you'll:

• Learn how Transformers are used as the basic building blocks of modern LLMs for NLP applications.
• Learn how self-supervision improves upon the Transformer architecture in BERT, Megatron, and other LLM variants for superior NLP results.
• Use pretrained, modern LLM models to solve multiple NLP tasks such as text classification, NER, and question answering.
• Manage inference challenges and deploy refined models for live applications.

Prerequisite(s):

• Python programming experience.
• Basic understanding of neural networks.
• A fundamental understanding of a deep learning framework such as TensorFlow or PyTorch.

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA certificate to recognize their subject matter competency and support professional career growth.

*IMPORTANT: Seats are limited, so reserve your spot today. Please note these workshops are not recorded and no shows are not eligible for a refund.