Transformer Models and BERT Model - 한국어

Coursera MOOC / Non-credit USD 49

Enroll now →

About this course

이 과정은 Transformer 아키텍처와 BERT(Bidirectional Encoder Representations from Transformers) 모델을 소개합니다. 셀프 어텐션 메커니즘 같은 Transformer 아키텍처의 주요 구성요소와 이 아키텍처가 BERT 모델 빌드에 사용되는 방식에 관해 알아봅니다. 또한 텍스트 분류, 질문 답변, 자연어 추론과 같이 BERT를 활용할 수 있는 다양한 작업에 대해서도 알아봅니다. 이 과정은 완료하는 데 대략 45분이 소요됩니다.

What you'll learn

understanding of Transformer architecture
knowledge of the BERT model
ability to apply BERT to text classification tasks
insight into using BERT for question answering and natural language inference

Skills you'll gain

deep learning artificial intelligence machine learning nlp natural language processing text classification question answering bert transformer models self-attention

Related courses

Generative AI for Executives and Business Leaders

Intelligent Agents and Search Algorithms

CPMAI Exam: 570+ Updated Practice Questions for 2026

Generative AI Cybersecurity & Privacy for Leaders

AI in Financial Services: Foundations through future trends

Introduction to Business Analytics: Communicating with Data

Internet of Things and AI Cloud

AI and Sustainability: Leading Responsibly

Course details are provided by the platform and may change — always confirm on the provider's site. Links may be affiliate links.