Building Transformer-Based Natural Language Processing Applications

NVIDIA Deep Learning Institute

In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks.

Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized natural language processing (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 and named-entity recognition (NER) tasks. 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.When: Oct 24-25, 8:30 am - 12:30 pm (CT).Where: This workshop takes place virtually via Zoom. Registrants will receive the Zoom link in an email one week prior to the workshop.Learning Objectives<ul><li>Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers</li><li>See how Transformer architecture features, especially self-attention, are used to create language models without RNNs</li><li>Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results </li><li>Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering</li><li>Manage inference challenges and deploy refined models for live applications </li></ul>Prerequisites<ul><li>Experience with Python coding and use of library functions and parameters </li><li>Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras</li><li>Basic understanding of neural networks</li></ul>Suggested materials to satisfy prerequisites: <a href="https://docs.python.org/3/tutorial/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Python Tutorial</a>, <a href="https://docs.python.org/3/tutorial/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Overview of Deep Learning Frameworks</a>, <a href="https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">PyTorch Tutorial</a>, <a href="https://developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Deep Learning in a Nutshell</a>, <a href="https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a21323eu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Deep Learning Demystified</a>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 server in the cloud.Agenda: To review the full workshop agenda, please visit <a href="https://www.nvidia.com/en-us/training/instructor-led-workshops/natural-language-processing/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">https://www.nvidia.com/en-us/training/instructor-led-workshops/natural-language-processing/</a>. 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.

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Building Transformer-Based Natural Language Processing Applications

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Building Transformer-Based Natural Language Processing Applications

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