We may earn an affiliate commission when you visit our partners.
Attention Mechanism - 한국어
Google Cloud Training
Google Cloud Training
이 과정에서는 신경망이 입력 시퀀스의 특정 부분에 집중할 수 있도록 하는 강력한 기술인 주목 메커니즘을 소개합니다. 주목 메커니즘의 작동 방식과 이 메커니즘을 다양한 머신러닝 작업(기계 번역, 텍스트 요약, 질문 답변 등)의 성능을 개선하는 데 활용하는 방법을 알아봅니다.
Register for this course and see more details by visiting:
OpenCourser.com/course/5fzxsr/attention
What's inside
Syllabus
주목 메커니즘 소개
이 모듈에서는 주목 메커니즘의 작동 방식과 이 메커니즘을 다양한 머신러닝 작업(기계 번역, 텍스트 요약, 질문 답변 등)의 성능을 개선하는 데 활용하는 방법을 알아봅니다.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces attention mechanisms, a powerful tool for enhancing sequence classification tasks
Covers a range of machine learning tasks where attention mechanisms improve performance, including machine translation, text summarization, and question answering
Save this course
Save Attention Mechanism - 한국어 to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Attention Mechanism - 한국어.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Attention Mechanism - 한국어 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning models for various applications. Attention Mechanisms are playing an increasingly significant role in machine learning, improving model performance and efficiency. By taking this course, you can gain a strong foundation in Attention Mechanisms and become a highly effective Machine Learning Engineer.
Natural Language Processing Engineer
Natural Language Processing Engineer
As a Natural Language Processing Engineer, you'll develop and implement systems that allow computers to understand and generate human language. Attention Mechanisms play a crucial role in understanding the context and meaning of text. This course can enhance your knowledge of Attention Mechanisms and their role in NLP, which is vital for developing advanced NLP systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. Attention Mechanisms are at the core of many AI applications, as they mimic the human ability to focus and prioritize information. By taking this course, you can acquire specialized knowledge in Attention Mechanisms and become a highly sought-after AI Engineer.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models for various applications. Attention Mechanisms are vital in deep learning, allowing models to concentrate on important features and relationships. This course can equip you with the expertise in Attention Mechanisms, making you a more proficient Deep Learning Engineer.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers explore and develop new techniques for computer systems to learn from data and identify patterns. An understanding of Attention Mechanisms is essential in developing and improving machine learning algorithms. This course can help build a foundation in the use of Attention Mechanisms in machine learning, enabling you to develop the most effective models.
Research Scientist
Research Scientist
As a Research Scientist, you will be at the forefront of developing new technologies and pushing the boundaries of knowledge. Attention Mechanisms are gaining prominence in various research fields, including computer vision, natural language processing, and machine learning. This course will provide you with a solid foundation in Attention Mechanisms, preparing you for groundbreaking research.
Computational Linguist
Computational Linguist
As a Computational Linguist, you will study the computational aspects of language and develop tools to process and analyze language data. Attention Mechanisms have revolutionized natural language processing tasks like machine translation and text summarization. This course will provide you with the knowledge and skills to develop advanced NLP applications.
Data Scientist
Data Scientist
Data Scientists analyze data to extract insights and develop predictive models. Attention Mechanisms are becoming increasingly important in data science, as they allow models to focus on specific parts of the data, leading to more accurate predictions. By taking this course, you can enhance your data science skills and stay ahead of the curve.
Computer Scientist
Computer Scientist
As a Computer Scientist, you will be involved in various aspects of computing, including theory, design, development, and application. Attention Mechanisms are becoming essential in many areas of computer science, such as machine learning, natural language processing, and computer vision. This course will provide you with a comprehensive understanding of Attention Mechanisms, giving you an advantage in your career.
Data Analyst
Data Analyst
As a Data Analyst, you will analyze data to extract insights and identify patterns. Attention Mechanisms are becoming more prevalent in data analysis, enabling analysts to focus on critical data points and relationships. By taking this course, you can enhance your data analysis skills and become more efficient in uncovering valuable insights.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. Attention Mechanisms enable robots to focus their sensors and decision-making on relevant information. Taking this course can equip you with the expertise in Attention Mechanisms, giving you an edge in developing next-generation robotic systems.
Computer Vision Scientist
Computer Vision Scientist
Computer Vision Scientists are responsible for developing and researching theories and algorithms for computer vision technology to be implemented in software and hardware. It involves using Attention Mechanisms to provide computers with the ability to "see" and identify objects, just as humans do. This course can help build a foundation in the principles of Attention Mechanisms, which is crucial for developing advanced computer vision systems.
Software Engineer
Software Engineer
As a Software Engineer, you will utilize Attention Mechanisms in developing various applications, from facial recognition software to language translation tools. By taking this course, you can gain a deeper understanding of Attention Mechanisms and their practical applications, making you a more valuable asset to your team.
Business Analyst
Business Analyst
Business Analysts study and improve business processes and operations. Attention Mechanisms can be applied to business data to identify key factors and trends. This course may provide you with an understanding of Attention Mechanisms and their potential applications in business analysis.
Product Manager
Product Manager
Product Managers lead the development and launch of products. While this course is not directly related to product management, the concepts of Attention Mechanisms and focusing on specific aspects of data may provide valuable insights for understanding user behavior and improving product design.
For more career information including salaries, visit:
OpenCourser.com/course/5fzxsr/attention
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Attention Mechanism - 한국어.
Save
주목 메커니즘을 포함한 딥러닝의 기본 원리를 다루는 책입니다. 이 책은 이 과정의 주제에 대한 백그라운드 지식을 제공하는 데 도움이 될 수 있습니다.
딥러닝의 고급 기술을 다루는 책으로, 주목 메커니즘에 대한 내용도 포함되어 있습니다. 이 책은 이 과정의 추가적인 리소스로 사용할 수 있습니다.
주목 메커니즘을 포함한 머신러닝과 패턴 인식의 이론적 기초를 제공합니다. 이 책은 이 과정의 배경 지식을 넓히는 데 도움이 될 수 있습니다.
주목 메커니즘을 포함한 머신러닝의 이론적 기초를 다룹니다. 이 책은 이 과정의 이론적 측면에 대한 추가적인 지침을 제공하는 데 도움이 될 수 있습니다.
주목 메커니즘을 포함한 머신러닝의 확률론적 접근 방식을 제공합니다. 이 책은 이 과정의 이론적 측면에 대한 추가적인 통찰력을 제공하는 데 도움이 될 수 있습니다.
파이썬을 사용한 딥러닝을 다룹니다. 이 책은 이 과정의 실무적인 측면에 대한 추가적인 지침을 제공하는 데 도움이 될 수 있습니다.
파이썬을 사용한 자연어 처리를 다룹니다. 이 책은 이 과정의 응용 분야 중 자연어 처리에 대한 추가적인 통찰력을 제공하는 데 도움이 될 수 있습니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5fzxsr/attention
Share
Similar courses
Here are nine courses similar to
Attention Mechanism - 한국어.
모두를 위한 머신 러닝
OpenCourser.com/course/txt1ha/modureul
모두를 위한 머신 러닝
비즈니스와 사회의 글로벌 트렌드
OpenCourser.com/course/y3ucwp/bijeuniseuwa
비즈니스와 사회의 글로벌 트렌드
Transformer Models and BERT Model - 한국어
OpenCourser.com/course/jev9i5/transformer
Transformer Models and BERT Model - 한국어
응용 전산 유체 역학
OpenCourser.com/course/o9p0eo/eungyong
응용 전산 유체 역학
Introduction to Image Generation - 한국어
OpenCourser.com/course/uy5ch3/introduction
Introduction to Image Generation - 한국어
기술 지원 기초
OpenCourser.com/course/r1aqt6/gisul
기술 지원 기초
파이썬의 데이터 과학 소개
OpenCourser.com/course/njxcxb/paisseonyi
파이썬의 데이터 과학 소개
스토리텔링과 영향력: 임팩트 있게 소통하라
OpenCourser.com/course/kzenig/seutoritelringgwa
스토리텔링과 영향력: 임팩트 있게 소통하라
MATLAB을 사용한 프로그래밍 소개
OpenCourser.com/course/jevdp4/matlabeul
MATLAB을 사용한 프로그래밍 소개
Effort
1 week of study, 6-8 hours
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
한국어
Transcript
English
Good to know
Introduces attention mechanisms, a powerful tool for enhancing sequence classification tasks
Covers a range of machine learning tasks where attention mechanisms improve performance, including machine translation, text summarization, and question answering
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser