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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。
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OpenCourser.com/course/hujs6g/encoder
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Syllabus
编码器-解码器架构：概览
本单元简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Tailored to learners interested in machine learning, particularly for tasks involving sequence-to-sequence modeling
Provides a comprehensive introduction to encoder-decoder architectures, making it suitable for beginners in machine learning
Offers hands-on coding exercises in TensorFlow, allowing learners to apply their understanding practically
Save this course
Save Encoder-Decoder Architecture - 简体中文 to your list so you can find it easily later:
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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 Encoder-Decoder Architecture - 简体中文 with these
activities:
学习 TensorFlow 基础知识
Show steps
学习 TensorFlow 基础知识可以帮助你理解编码器-解码器模型的工作原理，并为后续课程内容奠定基础。
Browse courses on
TensorFlow
Show steps
-
完成 TensorFlow 官方教程，了解其基本概念和操作。
-
查阅 TensorFlow 文档，深入了解其功能和 API。
-
尝试一些简单的 TensorFlow 代码示例，练习使用该框架。
阅读《深度学习》，作者：Ian Goodfellow、Yoshua Bengio 和 Aaron Courville
Show steps
阅读这本经典著作将帮助你深入了解机器学习和深度学习的基本原理，这些原理在编码器-解码器架构中至关重要。
View
深度學習
on Amazon
Show steps
-
阅读第 1 章，了解机器学习和深度学习的基础知识。
-
阅读第 6 章，了解神经网络和深度学习模型。
完成 TensorFlow 入门教程
Show steps
完成这些教程将帮助你熟悉 TensorFlow 的基础知识，TensorFlow 是本课程中使用的机器学习库。
Show steps
-
完成 TensorFlow 入门教程的第一部分。
-
完成 TensorFlow 入门教程的第二部分。
Five other activities
Expand to see all activities and additional details
Show all eight activities
寻找编码器-解码器架构领域的导师
Show steps
寻找一位导师可以帮助你进一步深入了解编码器-解码器架构。
Show steps
-
向机器学习社区的人员寻求推荐。
-
参加有关编码器-解码器架构的活动或研讨会。
练习编码编码器-解码器模型
Show steps
练习编码编码器-解码器模型将帮助你加深对本课程中介绍的架构的理解。
Show steps
-
使用 TensorFlow 从头开始实现一个简单的编码器-解码器模型。
-
训练和评估你的编码器-解码器模型。
创建诗歌生成器
Show steps
创建诗歌生成器将帮助你应用你从本课程中学到的知识，并创建一个可以生成新诗歌的实际应用程序。
Show steps
-
设计诗歌生成器的架构。
-
使用 TensorFlow 编写诗歌生成器的代码。
-
部署诗歌生成器。
担任编码器-解码器架构的导师
Show steps
担任其他学习者的导师可以帮助你巩固你对编码器-解码器架构的理解。
Show steps
-
在论坛或讨论组中回答有关编码器-解码器架构的问题。
-
指导初学者使用 TensorFlow 构建编码器-解码器模型。
参加自然语言处理竞赛
Show steps
参加自然语言处理竞赛可以帮助你测试你的编码器-解码器架构技能。
Show steps
-
选择一个自然语言处理竞赛。
-
提交你的模型参加比赛。
学习 TensorFlow 基础知识
Show steps
学习 TensorFlow 基础知识可以帮助你理解编码器-解码器模型的工作原理，并为后续课程内容奠定基础。
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- 完成 TensorFlow 官方教程，了解其基本概念和操作。
- 查阅 TensorFlow 文档，深入了解其功能和 API。
- 尝试一些简单的 TensorFlow 代码示例，练习使用该框架。
阅读《深度学习》，作者：Ian Goodfellow、Yoshua Bengio 和 Aaron Courville
Show steps
阅读这本经典著作将帮助你深入了解机器学习和深度学习的基本原理，这些原理在编码器-解码器架构中至关重要。
View
深度學習
on Amazon
Show steps
Show steps
Show steps
- 阅读第 1 章，了解机器学习和深度学习的基础知识。
- 阅读第 6 章，了解神经网络和深度学习模型。
完成 TensorFlow 入门教程
Show steps
完成这些教程将帮助你熟悉 TensorFlow 的基础知识，TensorFlow 是本课程中使用的机器学习库。
Show steps
Show steps
Show steps
- 完成 TensorFlow 入门教程的第一部分。
- 完成 TensorFlow 入门教程的第二部分。
Five other activities
Expand to see all activities and additional details
Show all eight activities
寻找编码器-解码器架构领域的导师
Show steps
寻找一位导师可以帮助你进一步深入了解编码器-解码器架构。
Show steps
Show steps
Show steps
- 向机器学习社区的人员寻求推荐。
- 参加有关编码器-解码器架构的活动或研讨会。
练习编码编码器-解码器模型
Show steps
练习编码编码器-解码器模型将帮助你加深对本课程中介绍的架构的理解。
Show steps
Show steps
Show steps
- 使用 TensorFlow 从头开始实现一个简单的编码器-解码器模型。
- 训练和评估你的编码器-解码器模型。
创建诗歌生成器
Show steps
创建诗歌生成器将帮助你应用你从本课程中学到的知识，并创建一个可以生成新诗歌的实际应用程序。
Show steps
Show steps
Show steps
- 设计诗歌生成器的架构。
- 使用 TensorFlow 编写诗歌生成器的代码。
- 部署诗歌生成器。
担任编码器-解码器架构的导师
Show steps
担任其他学习者的导师可以帮助你巩固你对编码器-解码器架构的理解。
Show steps
Show steps
Show steps
- 在论坛或讨论组中回答有关编码器-解码器架构的问题。
- 指导初学者使用 TensorFlow 构建编码器-解码器模型。
参加自然语言处理竞赛
Show steps
参加自然语言处理竞赛可以帮助你测试你的编码器-解码器架构技能。
Show steps
Show steps
Show steps
- 选择一个自然语言处理竞赛。
- 提交你的模型参加比赛。
Career center
Learners who complete Encoder-Decoder Architecture - 简体中文 will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Working as a Natural Language Processing Engineer, you will design and implement Natural Language Processing (NLP) solutions across a wide range of applications. This course provides an introduction to encoder-decoder architecture, which is frequently used in NLP applications such as text summarization and question answering. This course would be useful for anyone working in the NLP field.
Data Scientist
Data Scientist
A Data Scientist collects and analyzes data of many types - structured and unstructured - to extract meaningful insights. In this role you would use your programming and statistical skills to develop encoding-decoder algorithms for text and image analysis, among other things. This course can help you learn the basics of building an encoder-decoder architecture in machine learning. This foundational knowledge may help you develop competitive algorithms for your future employers.
Computational Linguist
Computational Linguist
Computational Linguists design and develop linguistic models by applying principles of computer science, linguistics, and mathematics. This course is an introduction to encoder-decoder architecture, which is often used in the development of NLP models. This course may be helpful for computational linguists new to machine learning.
Data Analyst
Data Analyst
Data Analysts develop and build data models by analyzing data, defining requirements, and designing architecture. As a data analyst, you will likely use encoder-decoder architectures in text and image analysis. This course covers the basics of encoder-decoder architectures, which will help you develop a strong foundation for your data analyst role.
Product Manager
Product Manager
Product Managers define and oversee the development of new products. This course may be useful for product managers who plan to work on machine learning-powered products, as it will provide a foundation in encoder-decoder architecture, which is used in a wide array of text and image processing applications.
University Professor
University Professor
University Professors teach and conduct research at colleges and universities. This course would be appropriate for university professors in computer science or related disciplines. If you are planning a career in academia, this course may be useful as it will provide a foundation in encoder-decoder architecture, which is a frequently used technique in NLP, CV, and other areas of machine learning.
Research Scientist
Research Scientist
Research Scientists contribute to original research in a specialized field, such as machine learning, artificial intelligence, or natural language processing. This course may be useful for research scientists working in these fields as it provides a foundation in encoder-decoder architecture. A strong understanding of foundational machine learning architectures is helpful for conducting research in any of these fields.
Software Engineer
Software Engineer
Working as a Software Engineer, you would write software across various platforms, including desktop programs, cloud-based applications, and mobile apps. Given the course's focus on TensorFlow, it is reasonable to assume that this course may be useful as it will introduce you to the basics of encoder-decoder architecture and how to implement such architectures with TensorFlow.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you would design, implement, and deploy machine learning models across different applications and industries. This course is an introductory course to encoder-decoder architecture, which is a powerful architecture for text-based and sequence-based problem domains. This course may be useful as it will help you build a foundation in this important architecture.
Data Engineer
Data Engineer
As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure. This course may be helpful for data engineers who work with machine learning engineers or data scientists, as it will provide a foundation in encoder-decoder architecture and TensorFlow, which are both commonly used in machine learning pipelines.
Computer Programmer
Computer Programmer
Computer Programmers write and test code in a variety of languages. This course may be useful for computer programmers who work on NLP or CV projects, as it will help them build a foundational understanding of encoder-decoder architecture.
Business Analyst
Business Analyst
As a Business Analyst, you will analyze the needs of a business and propose plans to solve problems. This course may be useful for business analysts who plan to work with data scientists or machine learning engineers, as it can help them better understand foundational machine learning concepts and encoder-decoder architecture specifically.
Information Technology Specialist
Information Technology Specialist
Information Technology Specialists plan, implement, and maintain computer systems and networks. This course may be useful for IT specialists who work with machine learning engineers or data scientists, as it will help them build a foundational understanding of encoder-decoder architecture and how these models are used in practice.
Technical Writer
Technical Writer
Technical Writers create and edit technical documentation, such as user manuals, training materials, and product documentation. This course may be useful for technical writers who work in the computer science or machine learning fields, as it will provide context and background knowledge for the products and technologies they write about.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts provide expertise in mathematical and statistical methods to solve problems across a range of industries. Working in this role, you will likely use regression models and other machine learning techniques. This course may be helpful as it will introduce you to encoder-decoder architectures and serve as a good primer for other machine learning architectures and concepts.
For more career information including salaries, visit:
OpenCourser.com/course/hujs6g/encoder
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
Encoder-Decoder Architecture - 简体中文.
深入探讨了循环神经网络，这是编码器-解码器架构中的关键组件。它提供了循环神经网络的全面概述，包括 LSTM 和 GRU。
Save
提供了深度学习的全面概述，包括卷积神经网络、递归神经网络和变分自动编码器等相关概念。虽然它并非专门针对编码器-解码器架构，但它提供了深度学习的坚实基础，这是编码器-解码器架构的基础。
Save
提供了深度学习的全面概述，包括卷积神经网络、递归神经网络和变分自动编码器等相关概念。虽然它并非专门针对编码器-解码器架构，但它提供了深度学习的坚实基础，这是编码器-解码器架构的基础。
提供了机器翻译的深入概述，包括统计机器翻译和神经机器翻译。虽然它不是专门针对编码器-解码器架构，但它提供了机器翻译的坚实基础，这是编码器-解码器架构的一个重要应用领域。
本书是使用 Python 进行深度学习的经典教材，从基础概念到高级技术，对该领域进行了全面的介绍。对于想要了解编码器-解码器架构在 Python 中的实现的学习者来说，这是一本非常有价值的读物。
本书是自然语言处理领域的权威著作，涵盖了编码器-解码器架构在自然语言理解和生成任务中的应用。
本书是神经机器翻译领域的专著，为该课程中涉及的编码器-解码器架构在机器翻译中的应用提供了深入介绍。
本书提供了自然语言处理领域中神经网络模型的基础知识，有助于理解该课程中涉及的编码器-解码器架构。
本书是自然语言处理领域的经典教材，提供了该课程相关背景知识和基础算法介绍，适合作为补充读物。
本书提供了基于fastai和PyTorch框架进行深度学习的实践指南，有助于该课程中涉及的编码器-解码器架构的实现和实践。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hujs6g/encoder
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Level
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Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
中文
Transcript
English
Good to know
Tailored to learners interested in machine learning, particularly for tasks involving sequence-to-sequence modeling
Provides a comprehensive introduction to encoder-decoder architectures, making it suitable for beginners in machine learning
Offers hands-on coding exercises in TensorFlow, allowing learners to apply their understanding practically
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