BNLPANVNvidiaNV-BNLPA1.0<ul> <li>How transformers are used as the basic building blocks of modern LLMs for NLP applications</li><li>How self-supervision improves upon the transformer architecture in BERT, Megatron, and other LLM variants for superior NLP results</li><li>How to leverage pretrained, modern LLM models to solve multiple NLP tasks such as text classification, named-entity recognition (NER), and question answering</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><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><h5>Introduction</h5><ul> <li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul><h5>Introduction to Transformers</h5><ul> <li>Explore how the transformer architecture works in detail:</li><li>Build the transformer architecture in PyTorch.</li><li>Calculate the self-attention matrix.</li><li>Translate English to German with a pretrained transformer model.</li></ul><h5>Self-Supervision, BERT, and Beyond</h5><p>Learn how to apply self-supervised transformer-based models to concrete NLP tasks using NVIDIA NeMo:</p> <ul> <li>Build a text classification project to classify abstracts.</li><li>Build a NER project to identify disease names in text.</li><li>Improve project accuracy with domain-specific models.</li></ul><h5>Inference and Deployment for NLP </h5><ul> <li>Learn how to deploy an NLP project for live inference on NVIDIA Triton:</li><li>Prepare the model for deployment.</li><li>Optimize the model with NVIDIA&reg; TensorRT&trade;.</li><li>Deploy the model and test it.</li></ul><h5>Final Review</h5><ul> <li>Review key learnings and answer questions.</li><li>Complete the assessment and earn a certificate.</li><li>Take the workshop survey.</li><li>Learn how to set up your own environment and discuss additional resources and training.</li></ul>- How transformers are used as the basic building blocks of modern LLMs for NLP applications - How self-supervision improves upon the transformer architecture in BERT, Megatron, and other LLM variants for superior NLP results - How to leverage pretrained, modern LLM models to solve multiple NLP tasks such as text classification, named-entity recognition (NER), and question answering - Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering - Manage inference challenges and deploy refined models for live applications- 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 networksIntroduction - Meet the instructor. - Create an account at courses.nvidia.com/join Introduction to Transformers - Explore how the transformer architecture works in detail: - Build the transformer architecture in PyTorch. - Calculate the self-attention matrix. - Translate English to German with a pretrained transformer model. Self-Supervision, BERT, and Beyond Learn how to apply self-supervised transformer-based models to concrete NLP tasks using NVIDIA NeMo: - Build a text classification project to classify abstracts. - Build a NER project to identify disease names in text. - Improve project accuracy with domain-specific models. Inference and Deployment for NLP - Learn how to deploy an NLP project for live inference on NVIDIA Triton: - Prepare the model for deployment. - Optimize the model with NVIDIA® TensorRT™. - Deploy the model and test it. Final Review - Review key learnings and answer questions. - Complete the assessment and earn a certificate. - Take the workshop survey. - Learn how to set up your own environment and discuss additional resources and training.1 jour995.00995.00500.00995.00995.00420.00690.00995.00