
Online or onsite, instructor-led live Reinforcement Learning training courses demonstrate through interactive hands-on practice how to create and deploy a Reinforcement Learning system.
Reinforcement Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Indonesia onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
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The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
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Course: Deep Reinforcement Learning with Python
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Amr Alaa - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
learning new language.
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
helpful and good listener .. interactive
Ahmed El Kholy - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course
Mohamed Ghowaiba - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
the course is very interesting being the main focus nowdays
mohamed taher - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
The discussions to broaden our horizons
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
I like examples to explain
AUO友达光电(苏州)有限公司
Course: OptaPlanner in Practice
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Reinforcement Learning Course Outlines in Indonesia
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
- Apply advanced Reinforcement Learning algorithms to solve real-world problems.
- Build a Deep Learning Agent.
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
- Install and apply the libraries and programming language needed to implement Reinforcement Learning.
- Create a software agent that is capable of learning through feedback instead of through supervised learning.
- Program an agent to solve problems where decision making is sequential and finite.
- Apply knowledge to design software that can learn in a way similar to how humans learn.
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