Introduction to Transformer Models for NLP: Unit 1

Introduction to Transformer Models for NLP: Unit 1

本课程是 Introduction to Transformer Models for NLP 专项课程的一部分

位教师：Pearson

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1个模块

深入了解一个主题并学习基础知识。

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3 小时完成

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1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

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作业

3 项作业

授课语言：英语（English）

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本课程是 Introduction to Transformer Models for NLP 专项课程专项课程的一部分

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该课程共有1个模块

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.

This module explores the evolution of natural language processing (NLP) through the development and application of attention mechanisms and transformer architectures. Beginning with the history and foundational concepts of attention in language models, it delves into the transformative impact of transformers and their unique attention mechanisms. The module concludes with practical instruction on transfer learning, demonstrating how to fine-tune state-of-the-art pre-trained models like BERT and GPT using PyTorch to achieve advanced NLP results.