Sorry, this page is no longer available

We may earn an affiliate commission when you visit our partners.

Attention Mechanism - Português Brasileiro

Google Cloud Training

Google Cloud Training

Este curso é uma introdução ao mecanismo de atenção, uma técnica avançada que permite que as redes neurais se concentrem em partes específicas de uma sequência de entrada. Você vai entender como a atenção funciona e como ela pode ser usada para melhorar o desempenho de várias tarefas de machine learning (como tradução automática, resumo de texto e resposta a perguntas).

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by Google Cloud Training, who are recognized for their work in machine learning

Develops skills in using the attention mechanism, which is highly relevant to machine learning tasks

Suitable for beginners looking to build a foundation in machine learning

A caveat is that the course focuses on a single machine learning technique, so learners may need to supplement with other resources to gain a more comprehensive understanding of machine learning

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Attention mechanism: introductory course in portuguese

According to learners who would find this course valuable, this introductory course from Google Cloud is expected to offer a clear and focused overview of the attention mechanism, a crucial technique in modern machine learning. It aims to provide a solid foundation for understanding how neural networks process sequences, particularly useful for tasks like machine translation. Delivered entirely in Brazilian Portuguese, it serves as an accessible starting point. However, students seeking deep dives or extensive hands-on implementations might find its scope limited.

Expected high-quality content and production from Google Cloud.

"I trust Google Cloud to deliver accurate, up-to-date, and highly relevant technical content."

"The production value of Google Cloud courses is consistently high, making the learning experience smooth and enjoyable."

"Knowing it's from Google Cloud gives me confidence in the course material's reliability and its industry relevance."

Offers a concise and understandable overview of the attention mechanism.

"The course perfectly introduced me to the core concepts of attention mechanism without getting bogged down in excessive detail."

"I now have a solid understanding of how attention works and its fundamental applications in machine learning."

"For someone new to attention mechanisms, this provides a really strong and accessible starting point."

Highly beneficial for native Portuguese speakers seeking technical content.

"It's great to have a course on such an advanced topic available entirely in my native Brazilian Portuguese."

"I appreciate the effort in providing this technical content without a language barrier, making it much easier to grasp."

"Finally, a clear explanation of attention mechanisms that I can easily follow in Portuguese."

Focused on introduction, may lack advanced details and practical application.

"While the introduction is good, I was hoping for more in-depth examples and coding exercises to solidify my understanding."

"I feel it's a bit too theoretical for my needs; I'd love to see more hands-on implementation details and real-world scenarios."

"This course is a good start, but I'll definitely need to look for additional resources for advanced topics and practical application."

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 - Português Brasileiro with these activities:

Read "Neural Networks and Deep Learning" by Michael Nielsen

Show steps

Go over the basics of neural networks and deep learning to prepare for this course

Show steps

Read Chapters 1-5
Complete the exercises in Chapters 1-5

Follow the TensorFlow Tutorial on Attention Mechanisms

Show steps

Get hands-on experience with attention mechanisms and apply it with TensorFlow

Browse courses on Attention Mechanisms

Show steps

Complete the "Attention Mechanisms" tutorial on TensorFlow
Implement the attention mechanism in your own code

Solve practice problems on attention mechanisms

Show steps

Reinforce your understanding of attention mechanisms by solving practice problems

Browse courses on Attention Mechanisms

Show steps

Solve 10-15 practice problems on attention mechanisms
Review your solutions and identify areas for improvement

Two other activities

Expand to see all activities and additional details

Show all five activities

Create a blog post or video explaining attention mechanisms

Show steps

Deepen your understanding of attention mechanisms by explaining it to others

Browse courses on Attention Mechanisms

Show steps

Plan your blog post or video content
Write or record your content
Publish and promote your content

Build a machine learning model that uses attention mechanisms

Show steps

Get hands-on experience with attention mechanisms by building a real-world project

Browse courses on Attention Mechanisms

Show steps

Define the problem you want to solve
Design and implement your model
Train and evaluate your model
Interpret your results

Career center

Learners who complete Attention Mechanism - Português Brasileiro will develop knowledge and skills that may be useful to these careers:

Pesquisador de Aprendizado de Máquina

Um Pesquisador de Aprendizado de Máquina pode usar o mecanismo de atenção para explorar novas técnicas e algoritmos de aprendizado de máquina. Este curso pode ajudar os Pesquisadores de Aprendizado de Máquina a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles avancem o campo do aprendizado de máquina.

See salaries and explore the career path for Pesquisador de Aprendizado de Máquina

Engenheiro de Aprendizado de Máquina

Um Engenheiro de Aprendizado de Máquina pode usar o mecanismo de atenção para projetar e implementar soluções de aprendizado de máquina. Este curso pode ajudar os Engenheiros de Aprendizado de Máquina a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles desenvolvam soluções mais eficazes.

See salaries and explore the career path for Engenheiro de Aprendizado de Máquina

Cientista de Dados

Um Cientista de Dados pode usar o mecanismo de atenção para desenvolver e melhorar modelos de aprendizado de máquina. Este curso pode ajudar os Cientistas de Dados a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles criem modelos mais precisos e eficientes.

See salaries and explore the career path for Cientista de Dados

Analista de Dados

Um Analista de Dados pode usar o mecanismo de atenção para identificar padrões e tendências em grandes conjuntos de dados. Este curso pode ajudar os Analistas de Dados a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, melhorando sua capacidade de extrair insights valiosos dos dados.

See salaries and explore the career path for Analista de Dados

Engenheiro de Software

Um Engenheiro de Software pode usar o mecanismo de atenção para desenvolver e melhorar aplicativos de software. Este curso pode ajudar os Engenheiros de Software a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles criem aplicativos mais eficientes e fáceis de usar.

See salaries and explore the career path for Engenheiro de Software

Analista de negocios

Um Analista de Negócios pode usar o mecanismo de atenção para identificar oportunidades e riscos para uma organização. Este curso pode ajudar os Analistas de Negócios a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles forneçam insights mais valiosos para a tomada de decisões.

See salaries and explore the career path for Analista de negocios

Gerente de Produto

Um Gerente de Produto pode usar o mecanismo de atenção para entender as necessidades dos usuários e desenvolver produtos que atendam a essas necessidades. Este curso pode ajudar os Gerentes de Produto a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles criem produtos mais bem-sucedidos.

See salaries and explore the career path for Gerente de Produto

Estrategista de Marketing

Um Estrategista de Marketing pode usar o mecanismo de atenção para desenvolver e implementar campanhas de marketing mais eficazes. Este curso pode ajudar os Estrategistas de Marketing a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles alcancem um público mais amplo e envolvido.

See salaries and explore the career path for Estrategista de Marketing

Redator de Conteúdo

Um Redator de Conteúdo pode usar o mecanismo de atenção para criar conteúdo escrito que seja mais envolvente e informativo. Este curso pode ajudar os Redatores de Conteúdo a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles criem conteúdo que atinja um público mais amplo e os mantenha engajados.

See salaries and explore the career path for Redator de Conteúdo

Especialista em Experiência do Usuário

Um Especialista em Experiência do Usuário pode usar o mecanismo de atenção para projetar e implementar interfaces de usuário mais fáceis de usar. Este curso pode ajudar os Especialistas em Experiência do Usuário a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles criem interfaces de usuário mais intuitivas e agradáveis.

See salaries and explore the career path for Especialista em Experiência do Usuário

Analista Financeiro

Um Analista Financeiro pode usar o mecanismo de atenção para identificar oportunidades e riscos de investimento. Este curso pode ajudar os Analistas Financeiros a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles tomem decisões de investimento mais informadas.

See salaries and explore the career path for Analista Financeiro

Consultor de Gestão

Um Consultor de Gestão pode usar o mecanismo de atenção para identificar e resolver problemas de negócios. Este curso pode ajudar os Consultores de Gestão a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles forneçam soluções mais eficazes para seus clientes.

See salaries and explore the career path for Consultor de Gestão

Professor

Um Professor pode usar o mecanismo de atenção para criar e ministrar aulas mais eficazes. Este curso pode ajudar os Professores a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles envolvam melhor seus alunos e os ajudem a aprender mais efetivamente.

See salaries and explore the career path for Professor

Médico

Um Médico pode usar o mecanismo de atenção para diagnosticar e tratar pacientes de forma mais eficaz. Este curso pode ajudar os Médicos a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles identifiquem e tratem doenças mais rapidamente e com mais precisão.

See salaries and explore the career path for Médico

Advogado

Um Advogado pode usar o mecanismo de atenção para pesquisar e apresentar argumentos jurídicos de forma mais eficaz. Este curso pode ajudar os Advogados a construir uma base para entender e aplicar o mecanismo de atenção em seu trabalho, permitindo que eles defendam melhor seus clientes e alcancem resultados mais favoráveis.

See salaries and explore the career path for Advogado

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 - Português Brasileiro.

Cover image

Cover image

Reinforcement Learning, second edition

Save

Provides a comprehensive overview of deep reinforcement learning, including the latest research and applications. It valuable resource for anyone who wants to learn more about these topics.

Reinforcement Learning, second edition: An...

Reinforcement Learning, second edition: An...

Cover image

Cover image

Save

Provides a comprehensive overview of deep learning, including the latest research and applications. It valuable resource for anyone who wants to learn more about this field.

Deep Learning (Adaptive Computation and Machine...

Deep Learning (Adaptive Computation and Machine...

Cover image

Cover image

Deep Learning with Python, Second Edition

Save

Provides a practical introduction to deep learning using Python. It valuable resource for anyone who wants to learn how to build and deploy deep learning models.

Deep Learning with Python, Second Edition

Deep Learning with Python, Second Edition

Cover image

Cover image

Natural Language Processing with Transformers,...

Save

Provides a practical introduction to natural language processing using transformers. It valuable resource for anyone who wants to learn how to build and deploy NLP models.

Natural Language Processing with Transformers,...

Natural Language Processing with Transformers,...

Cover image

Cover image

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

Provides a practical introduction to machine learning using Python. It valuable resource for anyone who wants to learn how to build and deploy machine learning models.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Cover image

Cover image

Neural Machine Translation

Save

Provides a comprehensive overview of neural machine translation, including the latest research and applications. It valuable resource for anyone who wants to learn more about this field.

Neural Machine Translation

Neural Machine Translation

Cover image

Cover image

Anaphora Resolution

Save

Provides a comprehensive overview of text summarization, including the latest research and applications. It valuable resource for anyone who wants to learn more about this field.

Anaphora Resolution

Anaphora Resolution

Share

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

Copy Link

Similar courses

Here are nine courses similar to Attention Mechanism - Português Brasileiro.

A Complete Reinforcement Learning System (Capstone)

A Complete Reinforcement Learning System (Capstone)

Generative AI: Elevate your Business Intelligence Career

Generative AI: Elevate your Business Intelligence Career

Creating a Supermarket App Using Python

Creating a Supermarket App Using Python

Ask Questions to Make Data-Driven Decisions

Ask Questions to Make Data-Driven Decisions

Predicting the Weather with Artificial Neural Networks

Predicting the Weather with Artificial Neural Networks

Machine Learning for Telecom Customers Churn Prediction

Machine Learning for Telecom Customers Churn Prediction

Deploying Machine Learning Models

Deploying Machine Learning Models

Data Mining Methods

Data Mining Methods

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner

Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections

14-day refund window on annual Coursera Plus and single courses

7-day free trial on monthly Coursera Plus subscriptions

Free course audit option for lectures and readings

Verified certificates from top universities & companies

OpenCourser Verified Partner Policy details last reviewed: 2026-10-22

Effort

1 week of study, 6-8 hours

Level

Intermediate

Via

Coursera

Institution

Google Cloud

Instructor

Google Cloud Training

Language

English

Transcript

Português

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by Google Cloud Training, who are recognized for their work in machine learning

Develops skills in using the attention mechanism, which is highly relevant to machine learning tasks

Suitable for beginners looking to build a foundation in machine learning

A caveat is that the course focuses on a single machine learning technique, so learners may need to supplement with other resources to gain a more comprehensive understanding of machine learning

Share this

Share to help others discover this course.

Link

Begin learning today

Enroll now to gain full access to Attention Mechanism - Português Brasileiro.

Enroll now Enroll in this course

Save for later

Add this course to your list. Find it anytime.

Save

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 - 2025 OpenCourser