Practical Deep Reinforcement Learning
5 Days
·Cohort-based Course
This course offers a deep dive into the practical aspects of reinforcement learning, tailored to your custom problems.
Practical Deep Reinforcement Learning
5 Days
·Cohort-based Course
This course offers a deep dive into the practical aspects of reinforcement learning, tailored to your custom problems.
Course overview
The transformation students will have in this course
In this course, students will transform from passive learners to active practitioners, mastering the tools and techniques necessary to implement and train custom reinforcement learning environments. By the end of the course, students will be proficient in using OpenAI Gym to create tailored environments and in applying TensorFlow to deploy cutting-edge reinforcement learning algorithms to these environments.
Who is this course for
01
Data Scientists and Machine Learning Engineers who already have a basic understanding of machine learning concepts.
02
Software Developers and Technical Product Managers interested in understanding and implementing AI-driven solutions to enhance products.
03
Academic Researchers and Graduate Students in computer science or related fields who require practical, hands-on experience with advanced AI
04
Industry Professionals in robotics, automation, healthcare, finance—gain a competitive edge with custom AI solutions.
Learn how to use the state-of-the-art deep reinforcement learning algorithms for your specific problems.
Understand the fundamental concepts of reinforcement learning.
Build a robust understanding of core reinforcement learning principles. Master the theoretical frameworks and mechanisms that underpin how agents learn and make decisions, setting a solid foundation for advanced applications.
Use OpenAI Gym to implement an environment tailored to your specific problems.
Acquire practical skills in environment setup and customization. Learn to design and manipulate OpenAI Gym environments that accurately simulate your specific challenges, enabling precise model training and testing.
Train the state-of-the-art deep reinforcement learning algorithms to for your specific problems.
Advance your capabilities in deploying and tuning complex models. Develop proficiency in training and optimizing deep reinforcement learning algorithms to effectively solve your targeted problems, ensuring high performance and adaptability.
Use pre-built environments such as DeepMind Control Suite and MuJoCo.
Enhance your technical expertise in using advanced simulation tools.Gain hands-on experience with leading industry-standard tools for modeling and testing, which improves your models' robustness and your ability to handle diverse scenarios.
This course includes
2 interactive live sessions
Lifetime access to course materials
6 in-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Practical Deep Reinforcement Learning
Course syllabus
Week 1
May
22
Practical Deep Reinforcement Learning 1
May
24
Practical Deep Reinforcement Learning 2
Practical Deep Reinforcement Learning
Meet your instructor
Navid Yousefabadi
An accomplished researcher and educator, Navid has cultivated a diverse academic and professional background spanning physics, quantum computing, and artificial intelligence. Originally graduating with a degree in physics from Shiraz University in 2009, Navid embarked on an international academic journey, moving to Canada to deepen their expertise in quantum computers at Dalhousie University where they completed their master's degree.
Driven by a passion for cutting-edge technology, Navid furthered their research in quantum computing as a Ph.D. student at the University of Calgary. This rigorous research laid a solid foundation for their transition into the fields of machine learning and data science. Since 2017, Navid has been immersed in these disciplines, demonstrating a keen ability to adapt and innovate. They also earned a master's degree in Data Science from the University of Calgary, underscoring their commitment to continual learning and expertise.
Currently, Navid serves as an instructor in artificial intelligence at the University of Calgary, where they inspire and educate the next generation of AI experts. Their courses cover a broad range of AI topics, with a particular focus on machine learning applications and theoretical advancements. Beyond the classroom, Navid actively contributes to the AI community, engaging in research that bridges theoretical underpinnings with practical implementations.
Navid's professional and academic journeys reflect a deep-seated dedication to advancing technology and education, marking them as a leading figure in the interdisciplinary realms of AI and data science.
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Practical Deep Reinforcement Learning
Course schedule
4 hours
Tuesday & Thursday
3:00pm - 5:00pm EST
Upcoming Sessions
May 21, 2024 - May 23, 2024
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
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