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Mat Leonard,
Miguel Morales,
Chhavi Yadav,
Dana Sheahan,
Cezanne Camacho,
Alexis Cook,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/2zyfkl/special
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What's inside
Syllabus
The dynamic programming setting is a useful first step towards tackling the reinforcement learning problem.
Good to know
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Know what's good ,
what to watch for , and
possible dealbreakers
Develops dynamic programming, which is a useful step towards reinforcement learning
Save this course
Save Special Topics in Deep Reinforcement Learning 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 Special Topics in Deep Reinforcement Learning with these
activities:
Read 'Introduction to Dynamic Programming' by Sutton and Barto
Show steps
Deepen your theoretical understanding by exploring a comprehensive textbook on dynamic programming.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting key concepts.
-
Complete the practice exercises and problems provided in the book.
Review prerequisite knowledge
Show steps
Refresh your understanding of topics that will be built upon in the course.
Browse courses on
Dynamic programming
Show steps
-
Review materials from a previous course or textbook on dynamic programming.
-
Solve practice problems on dynamic programming.
Engage in peer-to-peer learning
Show steps
Enhance understanding through discussions and knowledge sharing with fellow students.
Browse courses on
Dynamic programming
Show steps
-
Join or create a study group specifically for this course.
-
Meet regularly to discuss course concepts and work on problems together.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on dynamic programming
Show steps
Expand your knowledge by following expert-led tutorials and exploring additional resources.
Browse courses on
Dynamic programming
Show steps
-
Identify reputable online tutorials on dynamic programming.
-
Follow the tutorials, taking notes and implementing the techniques discussed.
Practice dynamic programming problems
Show steps
Strengthen your problem-solving skills and reinforce concepts through practice.
Browse courses on
Dynamic programming
Show steps
-
Identify a set of dynamic programming problems to solve.
-
Implement solutions to the problems using a programming language of your choice.
-
Analyze the correctness and efficiency of your solutions.
Build a dynamic programming application
Show steps
Apply your understanding by developing a practical application that utilizes dynamic programming.
Browse courses on
Dynamic programming
Show steps
-
Identify a problem that can be solved using dynamic programming.
-
Design and implement a solution to the problem.
-
Test and evaluate your application.
Develop a research project on dynamic programming
Show steps
Advance your knowledge by conducting original research on a specific topic related to dynamic programming.
Browse courses on
Dynamic programming
Show steps
-
Identify a research topic and formulate a hypothesis.
-
Conduct a literature review to gather background information.
-
Design and implement experiments or simulations to test your hypothesis.
-
Analyze the results and draw conclusions.
Read 'Introduction to Dynamic Programming' by Sutton and Barto
Show steps
Deepen your theoretical understanding by exploring a comprehensive textbook on dynamic programming.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, taking notes and highlighting key concepts.
- Complete the practice exercises and problems provided in the book.
Review prerequisite knowledge
Show steps
Refresh your understanding of topics that will be built upon in the course.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Review materials from a previous course or textbook on dynamic programming.
- Solve practice problems on dynamic programming.
Engage in peer-to-peer learning
Show steps
Enhance understanding through discussions and knowledge sharing with fellow students.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Join or create a study group specifically for this course.
- Meet regularly to discuss course concepts and work on problems together.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on dynamic programming
Show steps
Expand your knowledge by following expert-led tutorials and exploring additional resources.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify reputable online tutorials on dynamic programming.
- Follow the tutorials, taking notes and implementing the techniques discussed.
Practice dynamic programming problems
Show steps
Strengthen your problem-solving skills and reinforce concepts through practice.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify a set of dynamic programming problems to solve.
- Implement solutions to the problems using a programming language of your choice.
- Analyze the correctness and efficiency of your solutions.
Build a dynamic programming application
Show steps
Apply your understanding by developing a practical application that utilizes dynamic programming.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify a problem that can be solved using dynamic programming.
- Design and implement a solution to the problem.
- Test and evaluate your application.
Develop a research project on dynamic programming
Show steps
Advance your knowledge by conducting original research on a specific topic related to dynamic programming.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify a research topic and formulate a hypothesis.
- Conduct a literature review to gather background information.
- Design and implement experiments or simulations to test your hypothesis.
- Analyze the results and draw conclusions.
Career center
Learners who complete Special Topics in Deep Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers build software tools driven by machine learning and artificial intelligence. Having knowledge of deep reinforcement learning is vital to working in this career, especially when developing deep neural networks. This course in Special Topics in Deep Reinforcement Learning will help you understand the dynamic programming setting, which is a critical step in reinforcement learning and can help you advance in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. Deep reinforcement learning is an important field for a Machine Learning Engineer to have knowledge of, as it is used in many different types of machine learning models. This course will teach you the dynamic programming setting, which can help you develop better machine learning models and expand your knowledge as an Engineer.
Deep Reinforcement Learning Engineer
Deep Reinforcement Learning Engineer
Deep Reinforcement Learning Engineers design, develop, and implement deep reinforcement learning algorithms. This course will help you understand the dynamic programming setting, a critical part of deep reinforcement learning. Gaining this knowledge can provide you with an advantage while working as a Deep Reinforcement Learning Engineer and will help you advance your career further.
Robotics Engineer
Robotics Engineer
Robotics Engineers, as the title suggests, design, develop, build, test, and maintain robots. As an integral part of robotics, deep reinforcement learning can help you work on new robot designs. This course will teach you the dynamic programming setting, which is a useful foundation to have in this career field.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers explore the field of Artificial Intelligence by developing new theories and algorithms. Deep reinforcement learning is a vital part of this field and is used for a variety of applications, including robotics, natural language processing, and computer vision. This course will enhance your knowledge of deep reinforcement learning, which can help you expand your capabilities in this role.
NLP Engineer
NLP Engineer
NLP Engineers design, develop and implement natural language processing systems. Deep reinforcement learning is a critical part of this field and is used for a variety of applications, such as machine translation, text summarization, and question answering. This course will help provide you with a deep understanding of reinforcement learning, which is a vital skill in this career.
Computational Neuroscientist
Computational Neuroscientist
Computational Neuroscientists research the brain and nervous system using computational methods. Deep reinforcement learning is a useful tool in simulating brain activity and cognitive processes. This course can help you advance in this field by teaching you the dynamic programming setting that plays an important role in reinforcement learning.
Data Scientist
Data Scientist
Data Scientists analyze and interpret data in order to solve business problems. Deep reinforcement learning is an important field for a Data Scientist to have knowledge of, as it can help them analyze big data. This course will help you understand the dynamic programming setting, which is a critical step in reinforcement learning and can help you advance in this field.
Industrial Engineer
Industrial Engineer
Industrial Engineers improve processes and systems in a production environment. Deep reinforcement learning is a necessary part of this field, as it can be used for process optimization, resource allocation, and scheduling. This course will introduce you to the dynamic programming setting, a critical element of deep reinforcement learning. Learning this can help you advance your career in this field.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design, develop, and implement control systems. Deep reinforcement learning is a useful part of this field and can be used to control a variety of systems, including robots, vehicles, and industrial machinery. This course will introduce you to the dynamic programming setting, a critical element of deep reinforcement learning. Learning this can help you advance your career in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. Deep reinforcement learning is an important field for a Software Engineer to have knowledge of, as it is used in many different types of software applications. This course will teach you the dynamic programming setting, which can help you develop better software applications and enhance your career as a Software Engineer.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts identify and mitigate cybersecurity threats. Deep reinforcement learning is an important field for a Cybersecurity Analyst to have knowledge of, as it can be used to develop new security measures. This course will teach you the dynamic programming setting, a useful framework in reinforcement learning. By learning this, you can enhance your skill set and grow in this field.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks. Deep reinforcement learning may be useful in this role, as it can be used to develop risk management strategies. This course can teach you the dynamic programming setting, a useful foundation to have in this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use analytical methods to solve complex problems. Deep reinforcement learning may be useful in this role, as it can be applied to a variety of problems, such as supply chain management and resource allocation. This course can teach you the dynamic programming setting, a useful foundation to have in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. Deep reinforcement learning may be useful in this role, as it can be used to develop trading strategies. This course may help you in this field, as it will teach you the dynamic programming setting. This is a critical step in reinforcement learning that may provide you with an advantage in this role.
For more career information including salaries, visit:
OpenCourser.com/course/2zyfkl/special
Reading list
We've selected six 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
Special Topics in Deep Reinforcement Learning.
Classic introduction to reinforcement learning. It provides a comprehensive overview of the field, from the basics to the most advanced techniques.
Provides a concise overview of the algorithms used in reinforcement learning. It good resource for students who want to learn about the implementation details of these algorithms.
Save
Comprehensive overview of deep learning. It good resource for students who want to understand the theoretical foundations of this topic.
Provides a comprehensive overview of advanced deep learning techniques. It good resource for students who want to understand the theoretical foundations and practical applications of these techniques.
Provides a comprehensive overview of deep learning using Python. It good resource for students who want to learn how to implement deep learning models using Python.
Provides a comprehensive overview of machine learning using Python. It good resource for students who want to learn how to implement machine learning models using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2zyfkl/special
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Time
480 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Miguel Morales
Chhavi Yadav
Dana Sheahan
Cezanne Camacho
Alexis Cook
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Develops dynamic programming, which is a useful step towards reinforcement learning
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