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Dana Sheahen,
Arpan Chakraborty,
and
Kelvin Lwin
Unlock the world of AI with reinforcement learning. Explore diverse machine learning methods. Enroll today and learn online with Udacity's industry experts.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Reinforcement learning fundamentals
- Deep learning framework proficiency
- Neural network basics
- Object-oriented programming basics
- Intermediate Python
Read more
Unlock the world of AI with reinforcement learning. Explore diverse machine learning methods. Enroll today and learn online with Udacity's industry experts.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Reinforcement learning fundamentals
- Deep learning framework proficiency
- Neural network basics
- Object-oriented programming basics
- Intermediate Python
Read more
Unlock the world of AI with reinforcement learning. Explore diverse machine learning methods. Enroll today and learn online with Udacity's industry experts.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Reinforcement learning fundamentals
- Deep learning framework proficiency
- Neural network basics
- Object-oriented programming basics
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/cyz5b6/value
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What's inside
Syllabus
This lesson covers the study plan and prerequisites for this course.
Extend value-based reinforcement learning methods to complex problems using deep neural networks.
Train an agent to navigate a large world and collect yellow bananas, while avoiding blue bananas.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops advanced reinforcement learning applications in the real world
Extends value-based reinforcement learning methods to complex problems using deep neural networks
Provides opportunities to apply reinforcement learning techniques in real-world tasks
Suitable for learners with prior knowledge in reinforcement learning, deep learning, and object-oriented programming
Prerequisites include reinforcement learning fundamentals, deep learning framework proficiency, and neural network basics
Requires intermediate Python and fluency in written and spoken English
Save this course
Save Value Based Methods to your list so you can find it easily later:
Save
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 Value Based Methods with these
activities:
Review basics of reinforcement learning
Show steps
Reinforce your understanding of reinforcement learning concepts to prepare for this course.
Show steps
-
Review learning materials from previous courses or online resources.
-
Practice implementing basic reinforcement learning algorithms.
Seek guidance from experienced reinforcement learning practitioners
Show steps
Gain valuable insights and guidance from experienced professionals in the field.
Show steps
-
Identify potential mentors who have expertise in reinforcement learning.
-
Reach out to them and express your interest in their mentorship.
-
Establish regular communication and seek their advice on your learning journey.
Attend industry meetups or conferences on reinforcement learning
Show steps
Connect with experts and stay updated on the latest developments in reinforcement learning.
Browse courses on
Deep Learning Applications
Show steps
-
Identify relevant industry meetups or conferences.
-
Register and attend the events.
-
Network with professionals, share knowledge, and learn from others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a blog post or article on reinforcement learning
Show steps
Enhance your understanding of reinforcement learning by explaining it to others.
Browse courses on
Deep Learning Applications
Show steps
-
Choose a specific reinforcement learning topic or application to focus on.
-
Research and gather information from reliable sources.
-
Organize your thoughts and structure your content in a clear and engaging way.
-
Write your blog post or article, ensuring it is well-written and accessible to your target audience.
Solve reinforcement learning coding challenges
Show steps
Test and improve your reinforcement learning coding skills by solving challenges.
Show steps
-
Find online platforms or coding challenges that offer reinforcement learning problems.
-
Select challenges that align with the topics covered in the course.
-
Implement solutions using the appropriate deep learning frameworks and reinforcement learning algorithms.
-
Submit your solutions and review feedback to identify areas for improvement.
Contribute to open-source reinforcement learning projects
Show steps
Gain practical experience and contribute to the reinforcement learning community by working on open-source projects.
Show steps
-
Identify open-source reinforcement learning projects that align with your interests.
-
Review the project documentation and codebase.
-
Propose and implement improvements or new features.
-
Submit pull requests with your contributions.
Build a simple reinforcement learning agent
Show steps
Apply reinforcement learning concepts to a practical project by building a simple agent.
Browse courses on
Deep Neural Networks
Show steps
-
Define the problem and environment for your agent.
-
Design and implement a reinforcement learning algorithm for your agent.
-
Train and evaluate your agent's performance.
-
Iterate on your agent's design and training process to improve its performance.
Review basics of reinforcement learning
Show steps
Reinforce your understanding of reinforcement learning concepts to prepare for this course.
Show steps
Show steps
Show steps
- Review learning materials from previous courses or online resources.
- Practice implementing basic reinforcement learning algorithms.
Seek guidance from experienced reinforcement learning practitioners
Show steps
Gain valuable insights and guidance from experienced professionals in the field.
Show steps
Show steps
Show steps
- Identify potential mentors who have expertise in reinforcement learning.
- Reach out to them and express your interest in their mentorship.
- Establish regular communication and seek their advice on your learning journey.
Attend industry meetups or conferences on reinforcement learning
Show steps
Connect with experts and stay updated on the latest developments in reinforcement learning.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Identify relevant industry meetups or conferences.
- Register and attend the events.
- Network with professionals, share knowledge, and learn from others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a blog post or article on reinforcement learning
Show steps
Enhance your understanding of reinforcement learning by explaining it to others.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Choose a specific reinforcement learning topic or application to focus on.
- Research and gather information from reliable sources.
- Organize your thoughts and structure your content in a clear and engaging way.
- Write your blog post or article, ensuring it is well-written and accessible to your target audience.
Solve reinforcement learning coding challenges
Show steps
Test and improve your reinforcement learning coding skills by solving challenges.
Show steps
Show steps
Show steps
- Find online platforms or coding challenges that offer reinforcement learning problems.
- Select challenges that align with the topics covered in the course.
- Implement solutions using the appropriate deep learning frameworks and reinforcement learning algorithms.
- Submit your solutions and review feedback to identify areas for improvement.
Contribute to open-source reinforcement learning projects
Show steps
Gain practical experience and contribute to the reinforcement learning community by working on open-source projects.
Show steps
Show steps
Show steps
- Identify open-source reinforcement learning projects that align with your interests.
- Review the project documentation and codebase.
- Propose and implement improvements or new features.
- Submit pull requests with your contributions.
Build a simple reinforcement learning agent
Show steps
Apply reinforcement learning concepts to a practical project by building a simple agent.
Browse courses on
Deep Neural Networks
Show steps
Show steps
Show steps
- Define the problem and environment for your agent.
- Design and implement a reinforcement learning algorithm for your agent.
- Train and evaluate your agent's performance.
- Iterate on your agent's design and training process to improve its performance.
Career center
Learners who complete Value Based Methods will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, test, and maintain machine learning systems that can learn from data and make predictions. They work on a variety of projects, such as developing self-driving cars, improving search engines, and automating customer service. This course can help you become a Machine Learning Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many machine learning applications.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They work on a variety of projects, such as developing predictive models, identifying customer trends, and optimizing marketing campaigns. This course can help you become a Data Scientist by teaching you the fundamentals of reinforcement learning, which is a key technique used in many data science applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of projects, such as developing web applications, mobile apps, and enterprise software. This course can help you become a Software Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many software engineering applications.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new AI techniques and algorithms. They work on a variety of projects, such as developing self-driving cars, improving search engines, and automating customer service. This course can help you become an Artificial Intelligence Researcher by teaching you the fundamentals of reinforcement learning, which is a key technique used in many AI applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop, test, and maintain natural language processing systems. They work on a variety of projects, such as developing language translation systems, chatbots, and search engines. This course can help you become a Natural Language Processing Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many natural language processing applications.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop, test, and maintain computer vision systems. They work on a variety of projects, such as developing self-driving cars, improving search engines, and automating customer service. This course can help you become a Computer Vision Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many computer vision applications.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers develop, test, and maintain deep learning systems. They work on a variety of projects, such as developing self-driving cars, improving search engines, and automating customer service. This course can help you become a Deep Learning Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many deep learning applications.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. They work on a variety of projects, such as developing marketing campaigns, improving customer service, and optimizing operations. This course can help you become a Data Analyst by teaching you the fundamentals of reinforcement learning, which is a key technique used in many data analysis applications.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. They work on a variety of projects, such as developing self-driving cars, improving search engines, and automating customer service. This course can help you become a Robotics Engineer by teaching you the fundamentals of reinforcement learning, which is a key technique used in many robotics applications.
Business Analyst
Business Analyst
Business Analysts help businesses identify and solve problems. They work on a variety of projects, such as developing new products, improving customer service, and optimizing operations. This course can help you become a Business Analyst by teaching you the fundamentals of reinforcement learning, which is a key technique used in many business analysis applications.
Operations Manager
Operations Manager
Operations Managers plan and execute operations. They work on a variety of projects, such as developing new operations, improving customer service, and optimizing operations. This course may be useful for Operations Managers who want to learn about reinforcement learning and how it can be used to improve operations.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work on a variety of projects, such as developing new products, improving customer service, and optimizing operations. This course may be useful for Project Managers who want to learn about reinforcement learning and how it can be used to improve project outcomes.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. They work on a variety of projects, such as developing new marketing campaigns, improving customer service, and optimizing operations. This course may be useful for Marketing Managers who want to learn about reinforcement learning and how it can be used to improve marketing campaigns.
Sales Manager
Sales Manager
Sales Managers plan and execute sales campaigns. They work on a variety of projects, such as developing new sales campaigns, improving customer service, and optimizing operations. This course may be useful for Sales Managers who want to learn about reinforcement learning and how it can be used to improve sales performance.
Product Manager
Product Manager
Product Managers develop and manage products. They work on a variety of projects, such as developing new products, improving customer service, and optimizing operations. This course may be useful for Product Managers who want to learn about reinforcement learning and how it can be used to improve product development.
For more career information including salaries, visit:
OpenCourser.com/course/cyz5b6/value
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
Value Based Methods.
Provides a comprehensive overview of reinforcement learning, covering both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of reinforcement learning and optimal control. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of adaptive control of Markov processes. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of stochastic optimization and control. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of reinforcement learning for robotics. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of Markov decision processes. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of machine learning for robotics. It covers both the theoretical foundations and practical algorithms. It valuable resource for anyone interested in learning about this field.
Provides a comprehensive overview of mathematics for machine learning. It covers many of the underlying math concepts used in (deep) reinforcement learning.
Provides a comprehensive overview of probabilistic robotics. It covers many of the underlying math concepts used in (deep) reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cyz5b6/value
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Effort
4 weeks
Via
Udacity
Instructors
Dana Sheahen
Arpan Chakraborty
Kelvin Lwin
Language
English
Good to know
Develops advanced reinforcement learning applications in the real world
Extends value-based reinforcement learning methods to complex problems using deep neural networks
Provides opportunities to apply reinforcement learning techniques in real-world tasks
Suitable for learners with prior knowledge in reinforcement learning, deep learning, and object-oriented programming
Prerequisites include reinforcement learning fundamentals, deep learning framework proficiency, and neural network basics
Requires intermediate Python and fluency in written and spoken English
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