This course covers the development of natural language processing (NLP), starting with basic concepts and moving to modern transformer architectures. You will learn about attention mechanisms and their impact on language modeling, as well as the details of transformer models, including scaled dot product attention and multi-headed attention. The course includes practical exercises in transfer learning using pre-trained models such as BERT and GPT, with instruction on fine-tuning these models for specific NLP tasks in PyTorch. By the end, you will understand the theory behind current NLP models and gain practical experience in applying them to real-world problems.
Introduction to Transformer Models for NLP: Unit 1
Introduction to Transformer Models for NLP: Unit 1
This course is part of Introduction to Transformer Models for NLP Specialization
Instructors: Pearson
Access provided by SGCSRC
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand the evolution of NLP architectures and the transformative impact of attention mechanisms.
Analyze the structure and mathematical foundations of transformer models, including scaled dot product and multi-headed attention.
Apply transfer learning techniques using pre-trained language models such as BERT and GPT.
Gain practical experience with PyTorch to fine-tune NLP models for custom tasks.
Skills you'll gain
Tools you'll learn
Details to know
Shareable certificate
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Assessments
3 assignments
Taught in English
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This course is part of the Introduction to Transformer Models for NLP Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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