We may earn an affiliate commission when you visit our partners.
Course image
Google Cloud Training

本课程将向您介绍注意力机制,这是一种强大的技术,可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理,以及如何使用它来提高各种机器学习任务的性能,包括机器翻译、文本摘要和问题解答。

Enroll now

Two deals to help you save

We found two deals and offers that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

What's inside

Syllabus

注意力机制简介
在本单元中,您将了解注意力的工作原理,以及如何使用它来提高各种机器学习任务的性能,包括机器翻译、文本摘要和问题解答。

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores attention mechanism, which is widely used in various areas such as natural language processing and computer vision
Taught by Google Cloud Training, renowned for its expertise in cloud computing and machine learning
Suitable for individuals with a foundational understanding of machine learning and deep learning
Delivers practical knowledge and skills in applying attention mechanisms to real-world machine learning problems
Provides hands-on exercises and labs to reinforce understanding and enhance practical skills

Save this course

Save Attention Mechanism - 简体中文 to your list so you can find it easily later:
Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Attention Mechanism - 简体中文 with these activities:
Organize your course materials
Stay organized and improve your learning experience by compiling your course materials
Show steps
  • Gather all your course materials, including notes, slides, and assignments
  • Create a system for organizing your materials, such as using folders or a note-taking app
  • Regularly review and update your organized materials
Review computer science and statistics basics
Refresh your understanding of fundamental computer science and statistics concepts
Browse courses on Computer Science
Show steps
  • Review key concepts in data structures and algorithms
  • Brush up on probability and statistical inference
  • Solve practice problems to reinforce your understanding
Read 'Neural Networks and Deep Learning' by Michael Nielsen
Gain a deeper understanding of the fundamentals of neural networks and deep learning, which are essential for understanding attention mechanisms
View Melania on Amazon
Show steps
  • Purchase or borrow the book
  • Read the book thoroughly, taking notes and highlighting important concepts
  • Complete the exercises and assignments in the book to reinforce your understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore tutorials on attention mechanisms
Deepen your understanding of attention mechanisms by following guided tutorials
Browse courses on Attention Mechanisms
Show steps
  • Search for online tutorials on attention mechanisms
  • Follow along with the tutorials and complete the exercises
Practice exercises on attention mechanisms
巩固你的注意力机制知识, 通过解决练习题和练习题
Browse courses on Attention Mechanisms
Show steps
  • Find online exercises or practice problems on attention mechanisms
  • Attempt to solve the problems, checking your answers against provided solutions
  • Identify areas where you need improvement and focus your practice accordingly
Create a presentation on attention mechanisms
Enhance your understanding of attention mechanisms by creating a presentation to explain the concepts to others
Browse courses on Attention Mechanisms
Show steps
  • Gather information and research attention mechanisms thoroughly
  • Organize your content into a logical flow
  • Create visual aids to illustrate the concepts clearly
  • Practice your presentation and get feedback from others
Mentor junior students in attention mechanisms
强化你的理解力机制知识, 通过指导初级学生
Browse courses on Attention Mechanisms
Show steps
  • Identify opportunities to mentor junior students, such as through online forums or study groups
  • Share your knowledge and experience with the students
  • Provide guidance and support as they learn about attention mechanisms

Career center

Learners who complete Attention Mechanism - 简体中文 will develop knowledge and skills that may be useful to these careers:
Research Scientist
Research Scientists conduct research to advance the field of machine learning. They develop new algorithms and techniques, and they test them on real-world data. This course may be useful for Research Scientists who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine learning systems.
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and maintaining machine learning models. They work with data scientists to understand the business problem, and then they design and implement machine learning solutions. This course may be useful for Machine Learning Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine learning models.
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. They work with businesses to help them understand their data and make better decisions. This course may be useful for Data Scientists who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine learning models.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with businesses to understand their needs, and then they design and implement software solutions. This course may be useful for Software Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine learning systems.
Machine Learning Architect
A Machine Learning Architect is a professional who designs, builds and maintains machine learning systems. They combine their knowledge of machine learning with their expertise in software engineering to create systems that can solve complex problems. This course may be useful for Machine Learning Architects who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine learning systems.
Text Summarization Engineer
Text Summarization Engineers develop and maintain text summarization systems. They work with businesses to understand their needs, and then they design and implement text summarization solutions. This course may be useful for Text Summarization Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of text summarization systems.
Computer Vision Engineer
Computer Vision Engineers develop and maintain computer vision systems. They work with businesses to understand their needs, and then they design and implement computer vision solutions. This course may be useful for Computer Vision Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of computer vision systems.
Natural Language Processing Engineer
Natural Language Processing Engineers develop and maintain natural language processing systems. They work with businesses to understand their needs, and then they design and implement natural language processing solutions. This course may be useful for Natural Language Processing Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of natural language processing systems.
Machine Translation Engineer
Machine Translation Engineers develop and maintain machine translation systems. They work with businesses to understand their needs, and then they design and implement machine translation solutions. This course may be useful for Machine Translation Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of machine translation systems.
Question Answering Engineer
Question Answering Engineers develop and maintain question answering systems. They work with businesses to understand their needs, and then they design and implement question answering solutions. This course may be useful for Question Answering Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of question answering systems.
Speech Recognition Engineer
Speech Recognition Engineers develop and maintain speech recognition systems. They work with businesses to understand their needs, and then they design and implement speech recognition solutions. This course may be useful for Speech Recognition Engineers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of speech recognition systems.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be useful for Product Managers who are looking to learn more about attention mechanisms and how they can be used to improve the performance of new products.
Computational Linguist
Computational Linguists study the structure and meaning of language using computational methods. They develop new algorithms and techniques for natural language processing, and they apply these techniques to a variety of real-world problems. This course may be useful for Computational Linguists who are looking to learn more about attention mechanisms and how they can be used to improve the performance of natural language processing systems.
Business Analyst
Business Analysts use business analysis techniques to understand the business needs of an organization. They work with stakeholders to define the scope of a project, and they develop and implement solutions to meet those needs. This course may be useful for Business Analysts who are looking to learn more about attention mechanisms and how they can be used to improve the performance of business analysis techniques.
Data Analyst
Data Analysts use data analysis techniques to extract insights from data. They work with businesses to help them understand their data and make better decisions. This course may be useful for Data Analysts who are looking to learn more about attention mechanisms and how they can be used to improve the performance of data analysis techniques.

Reading list

We've selected ten 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 - 简体中文.
本书是深度学习领域的权威著作,提供了对注意力机制和其他高级主题的全面介绍。
本书是深度学习领域的经典著作,为理解注意力机制提供了必要的背景知识。它涵盖了神经网络的理论基础和实际应用。
本书是理解注意力机制数学基础的重要资源。它提供了深度学习算法的详细推导和分析。
本书是机器学习领域的标准教科书,为理解注意力机制提供了必要的理论基础。
本书提供了一个实用的介绍,重点介绍了机器学习项目的实际应用。它涵盖了注意力机制在各种任务中的应用。
这本书提供了机器学习的可解释性技术,有助于理解注意力机制的内部工作原理。
这本书提供了神经机器翻译的基础知识,对于理解注意力机制在机器翻译中的应用非常有帮助。

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Attention Mechanism - 简体中文.
Transformer Models and BERT Model - 简体中文
Most relevant
Encoder-Decoder Architecture - 简体中文
Most relevant
Create Image Captioning Models - 简体中文
Most relevant
Attention Mechanism - 繁體中文
Most relevant
Responsible AI: Applying AI Principles with GC - 简体中文
Most relevant
水力学 | Hydraulics
Most relevant
系统平台与计算环境
Most relevant
离散数学
Most relevant
Transformer Models and BERT Model - 繁體中文
Most relevant
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