Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks
English | Tutorial | Size: 3.78 GB
10+ Hours of Video Instruction
Learn how to apply state-of-the-art transformer-based models including BERT and GPT to solve modern NLP tasks.
Overview
Introduction to Transformer Models for NLP LiveLessons provides a comprehensive overview of transformers and the mechanisms-attention, embedding, and tokenization-that set the stage for state-of-the-art NLP models like BERT and GPT to flourish. The focus for these lessons is providing a practical, comprehensive, and functional understanding of transformer architectures and how they are used to create modern NLP pipelines. Throughout this series, instructor Sinan Ozdemir will bring theory to life through illustrations, solved mathematical examples, and straightforward Python examples within Jupyter notebooks.
All lessons in the course are grounded by real-life case studies and hands-on code examples. After completing this lesson, you will be in a great position to understand and build cutting-edge NLP pipelines using transformers. You will also be provided with extensive resources and curriculum detail which can all be found at the course’s GitHub repository.
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