Dozentengeführte Reinforcement Learning Präsenzschulungen in Schweiz.
Erfahrungsberichte
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AUO友达光电（苏州）有限公司
Kurs: OptaPlanner in Practice
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Information
Amr Alaa - FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Neue Sprache lernen.
FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Betreffungsprodukt Wissenszeit
Aly Saleh - FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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hilfreich und guter Listener .. Interactive
Ahmed El Kholy - FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Ahmed war sehr interaktiv und machte nichts dagegen, irgendeine Art von Fragen gut zu präsentieren und die glatte Fluss des Kurs
Mohamed Ghowaiba - FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Der Kurs ist sehr interessant, der Tag des Schwerpunkts
mohamed taher - FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Die Diskussionen, um unseren Horizont zu erweitern
FAB banak Egypt
Kurs: Introduction to Data Science and AI (using Python)
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Reinforcement Learning Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
21 Stunden
This instructor-led, live training in Schweiz (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
- 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.
By the end of this training, participants will be able to:
- 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.
21 Stunden
This instructor-led, live training in Schweiz (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.
By the end of this training, participants will be able to:
- 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.
By the end of this training, participants will be able to:
- 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.
7 Stunden
This instructor-led, live training in Schweiz (online or onsite) is aimed at researchers and developers who wish to install, configure, customize, and implement OpenAI Gym to quickly develop reinforcement learning algorithms.
By the end of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimize tasks and achieve maximum results.
By the end of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimize tasks and achieve maximum results.
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