2021-10-19 Building Transformer-Based Natural Language Processing Applications (hdli1w21)

Contents

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, analyse, and generate text-based 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 course, 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 analyse 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.

The course is co-organised by LRZ and NVIDIA Deep Learning Institute (DLI). NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.

Learning Objectives

By participating in this course, you’ll be able to:

• Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers,
• See how Transformer architecture features, especially self-attention, are used to create language models without RNNs,
• Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results,
• 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.

Important information

After you are accepted, please create an account under courses.nvidia.com/join.

Ensure your laptop / PC will run smoothly by going to http://websocketstest.com/ Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).If there are issues with WebSockets, try updating your browser. If you have any questions, please contact Marjut Dieringer at mdieringer"at"nvidia.com. 

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.

Hands-On

The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

Content Level

The content level of the course is broken down as: 

NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

Language

English

Lecturer

PD Dr. Juan Durillo Barrionuevo (LRZ, NVIDIA certified University Ambassador)

Prices and Eligibility

The course is open and free of charge for academic participants.

Registration

Please register with your official e-mail address to prove your affiliation.

Withdrawal Policy

See Withdrawal

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