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Este curso apresenta um resumo da arquitetura de codificador-decodificador, que é uma arquitetura de machine learning avançada e frequentemente usada para tarefas sequência para sequência (como tradução automática, resumo de textos e respostas a perguntas). Você vai conhecer os principais componentes da arquitetura de codificador-decodificador e aprender a treinar e disponibilizar esses modelos. No tutorial do laboratório relacionado, você vai codificar uma implementação simples da arquitetura de codificador-decodificador para geração de poesia desde a etapa inicial no TensorFlow.
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What's inside
Syllabus
Arquitetura de codificador-decodificador: Informações gerais
Este módulo apresenta um resumo da arquitetura de codificador-decodificador, que é uma arquitetura de machine learning avançada e frequentemente usada para tarefas sequência para sequência (como tradução automática, resumo de textos e respostas a perguntas). Você vai conhecer os principais componentes da arquitetura de codificador-decodificador e aprender a treinar e disponibilizar esses modelos. No tutorial do laboratório relacionado, você vai codificar uma implementação simples da arquitetura de codificador-decodificador para geração de poesia desde a etapa inicial no TensorFlow.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers the key components of encoder-decoder architectures, providing a strong foundation for understanding advanced machine learning techniques
Provides hands-on experience in building and deploying encoder-decoder models, enhancing learners' practical skills
Taught by Google Cloud Training, recognized for their expertise in cloud computing and machine learning
Suitable for individuals with an understanding of machine learning fundamentals and TensorFlow
May require additional resources or knowledge to complete the coding exercises effectively
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Attend industry events or conferences.
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Reach out to researchers or professionals working in the field.
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Join online communities or forums related to encoder-decoder models.
Create a Study Guide on Encoder-Decoder Architecture
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Encoder-Decoder Architecture
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Review your lecture notes and textbook readings.
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Identify the key concepts and terms related to encoder-decoder architecture.
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Organize the key concepts into a logical structure.
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Write out the study guide.
Solve Encoder-Decoder Model Problems
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Solving problems is a crucial way to test your understanding and identify areas where you need more practice.
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Find practice problems on encoder-decoder models online or in textbooks.
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Attempt to solve the problems on your own.
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Check your solutions against the provided answer key.
Four other activities
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Follow Tensorflow Tutorial on Encoder-Decoder Models
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TensorFlow provides comprehensive tutorials that can help you refine your understanding of the concepts covered in the course.
Browse courses on
Encoder-Decoder Architecture
Show steps
-
Visit the TensorFlow website and search for tutorials on encoder-decoder models.
-
Choose a tutorial that fits your level of experience.
-
Follow the steps in the tutorial to build and train an encoder-decoder model.
Implement Encoder-Decoder Architecture
Show steps
Coding a project from scratch is an excellent way to reinforce the concepts covered in the course.
Browse courses on
Encoder-Decoder Architecture
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-
Design the encoder network.
-
Implement the encoder network in your chosen programming language.
-
Design the decoder network.
-
Implement the decoder network.
-
Test the model on a dataset.
Attend a Workshop on Encoder-Decoder Architectures
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Attending a workshop allows you to learn from experts and engage in hands-on activities to reinforce your understanding.
Browse courses on
Encoder-Decoder Architecture
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Search for workshops on encoder-decoder architectures.
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Register for a workshop that fits your schedule and interests.
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Attend the workshop and participate actively.
Create a Presentation on Encoder-Decoder Applications
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Creating a presentation requires you to synthesize your knowledge and present it in a clear and concise manner.
Browse courses on
Encoder-Decoder Architecture
Show steps
-
Research various applications of encoder-decoder models.
-
Choose a specific application to focus on.
-
Develop a presentation outline.
-
Create slides for your presentation.
-
Practice your presentation.
Connect with Experts in Encoder-Decoder Architecture
Show steps
Connecting with experts can provide valuable insights and guidance to help you deepen your understanding.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Attend industry events or conferences.
- Reach out to researchers or professionals working in the field.
- Join online communities or forums related to encoder-decoder models.
Create a Study Guide on Encoder-Decoder Architecture
Show steps
Creating a study guide can help you organize your notes and identify key concepts for better retention.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Review your lecture notes and textbook readings.
- Identify the key concepts and terms related to encoder-decoder architecture.
- Organize the key concepts into a logical structure.
- Write out the study guide.
Solve Encoder-Decoder Model Problems
Show steps
Solving problems is a crucial way to test your understanding and identify areas where you need more practice.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Find practice problems on encoder-decoder models online or in textbooks.
- Attempt to solve the problems on your own.
- Check your solutions against the provided answer key.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Tensorflow Tutorial on Encoder-Decoder Models
Show steps
TensorFlow provides comprehensive tutorials that can help you refine your understanding of the concepts covered in the course.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Visit the TensorFlow website and search for tutorials on encoder-decoder models.
- Choose a tutorial that fits your level of experience.
- Follow the steps in the tutorial to build and train an encoder-decoder model.
Implement Encoder-Decoder Architecture
Show steps
Coding a project from scratch is an excellent way to reinforce the concepts covered in the course.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Design the encoder network.
- Implement the encoder network in your chosen programming language.
- Design the decoder network.
- Implement the decoder network.
- Test the model on a dataset.
Attend a Workshop on Encoder-Decoder Architectures
Show steps
Attending a workshop allows you to learn from experts and engage in hands-on activities to reinforce your understanding.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Search for workshops on encoder-decoder architectures.
- Register for a workshop that fits your schedule and interests.
- Attend the workshop and participate actively.
Create a Presentation on Encoder-Decoder Applications
Show steps
Creating a presentation requires you to synthesize your knowledge and present it in a clear and concise manner.
Browse courses on
Encoder-Decoder Architecture
Show steps
Show steps
Show steps
- Research various applications of encoder-decoder models.
- Choose a specific application to focus on.
- Develop a presentation outline.
- Create slides for your presentation.
- Practice your presentation.
Career center
Learners who complete Encoder-Decoder Architecture - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Text Summarization Engineer
Text Summarization Engineer
Text Summarization Engineers design and develop text summarization systems. They use their knowledge of text summarization algorithms and techniques to create systems that can summarize text into shorter, more concise summaries. This course may be useful for Text Summarization Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Text Summarization Engineers can improve the performance of their models on these tasks.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop natural language processing models. They use their knowledge of natural language processing techniques to solve problems in a variety of industries, including healthcare, finance, and manufacturing. This course may be useful for Natural Language Processing Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Natural Language Processing Engineers can improve the performance of their models on these tasks.
Machine Translation Engineer
Machine Translation Engineer
Machine Translation Engineers design and develop machine translation systems. They use their knowledge of machine translation algorithms and techniques to create systems that can translate text from one language to another. This course may be useful for Machine Translation Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Machine Translation Engineers can improve the performance of their models on these tasks.
Natural Language Generation Engineer
Natural Language Generation Engineer
Natural Language Generation Engineers design and develop natural language generation models. They use their knowledge of natural language processing techniques to create models that can generate text and speech. This course may be useful for Natural Language Generation Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Natural Language Generation Engineers can improve the performance of their models on these tasks.
Natural Language Understanding Engineer
Natural Language Understanding Engineer
Natural Language Understanding Engineers design and develop natural language understanding models. They use their knowledge of natural language processing techniques to create models that can understand the meaning of text and speech. This course may be useful for Natural Language Understanding Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Natural Language Understanding Engineers can improve the performance of their models on these tasks.
Question Answering Engineer
Question Answering Engineer
Question Answering Engineers design and develop question answering systems. They use their knowledge of question answering algorithms and techniques to create systems that can answer questions based on text or speech. This course may be useful for Question Answering Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Question Answering Engineers can improve the performance of their models on these tasks.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. They use their knowledge of artificial intelligence algorithms and techniques to create systems that can learn from data and make decisions. This course may be useful for Artificial Intelligence Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Artificial Intelligence Engineers can improve the performance of their models on these tasks.
Computational Linguist
Computational Linguist
Computational Linguists use computational methods to study language. They use their knowledge of linguistics and computer science to develop models that can understand and generate language. This course may be useful for Computational Linguists who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Computational Linguists can improve the performance of their models on these tasks.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers conduct research in machine learning. They use their knowledge of machine learning algorithms and techniques to develop new methods for solving problems. This course may be useful for Machine Learning Researchers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Machine Learning Researchers can improve the performance of their models on these tasks.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers design and develop speech recognition systems. They use their knowledge of speech recognition algorithms and techniques to create systems that can recognize spoken words and phrases. This course may be useful for Speech Recognition Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Speech Recognition Engineers can improve the performance of their models on these tasks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. They use their knowledge of machine learning algorithms and techniques to solve problems in a variety of industries, including healthcare, finance, and manufacturing. This course may be useful for Machine Learning Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Machine Learning Engineers can improve the performance of their models on these tasks.
Data Scientist
Data Scientist
Data Scientists use data to solve problems. They use their knowledge of statistics, machine learning, and data analysis techniques to extract insights from data. This course may be useful for Data Scientists who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Data Scientists can improve the performance of their models on these tasks.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They use their knowledge of statistics and data analysis techniques to extract insights from data. This course may be useful for Data Analysts who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Data Analysts can improve the performance of their models on these tasks.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, biology, and physics. They use their knowledge of scientific methods and research techniques to conduct experiments and collect data. This course may be useful for Research Scientists who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Research Scientists can improve the performance of their models on these tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development techniques to create software that meets the needs of users. This course may be useful for Software Engineers who want to learn more about encoder-decoder architecture. This architecture is commonly used in natural language processing tasks, such as machine translation and text summarization. By understanding encoder-decoder architecture, Software Engineers can improve the performance of their models on these tasks.
For more career information including salaries, visit:
OpenCourser.com/course/s32x20/encoder
Reading list
We've selected nine 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 - Português Brasileiro.
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Provides a comprehensive overview of deep learning, covering the latest advances and techniques. It valuable resource for researchers and practitioners alike.
Provides a comprehensive overview of speech and language processing, covering a variety of topics, including speech recognition, natural language understanding, and speech synthesis. It valuable resource for researchers and practitioners alike.
Provides a practical guide to Keras, a high-level neural networks API, written in Python. It valuable resource for anyone interested in learning more about Keras.
Provides a practical guide to TensorFlow, a popular deep learning framework. It valuable resource for anyone interested in learning more about TensorFlow.
Provides a practical guide to data science, covering a variety of topics, including data collection, data cleaning, data analysis, and data visualization. It valuable resource for anyone interested in learning more about data science.
Provides a practical guide to deep learning with Python, covering a variety of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about deep learning.
Provides a practical guide to natural language processing with Python, covering a variety of topics, including text classification, language modeling, and machine translation. It valuable resource for anyone interested in learning more about natural language processing.
Provides a practical guide to machine learning with Python, covering a variety of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone interested in learning more about machine learning.
Provides a practical guide to Python for data analysis, covering a variety of topics, including data cleaning, data manipulation, and data visualization. It valuable resource for anyone interested in learning more about Python for data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s32x20/encoder
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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
Português
Good to know
Covers the key components of encoder-decoder architectures, providing a strong foundation for understanding advanced machine learning techniques
Provides hands-on experience in building and deploying encoder-decoder models, enhancing learners' practical skills
Taught by Google Cloud Training, recognized for their expertise in cloud computing and machine learning
Suitable for individuals with an understanding of machine learning fundamentals and TensorFlow
May require additional resources or knowledge to complete the coding exercises effectively
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