NVIDIA Workshop

Sponsored by NSCC

Applications for natural language processing (NLP) have exploded in the past decade. With the proliferation of AI assistants and organizations infusing their businesses with more interactive human-machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate textbased data is essential. Modern techniques can capture the nuance, context, and sophistication of language, just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more.

Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized 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 tasks, such as categorizing documents. You’ll also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and 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.

Workshop Information and Prerequisites:

Duration: 8 hours

Prerequisites:

• 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.

Tools, libraries, and frameworks: PyTorch, pandas, NVIDIA NeMo™, NVIDIA Triton™ Inference Server

Assessment type:

• Skills-based coding assessments evaluate students’ ability to build an NLP task, including a neural module pipeline and training. 

• Multiple-choice questions evaluate students’ understanding of the NLP concepts presented in the class.

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.

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 workstation in the cloud.

Language: English