Corsi di formazione su Reinforcement Learning condotti da un formatore locale in diretta a Italia.
Recensioni
Mi piacciono gli esempi da spiegare
AUO友达光电（苏州）有限公司
Corso: OptaPlanner in Practice
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informazioni
Amr Alaa - FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Imparare la nuova lingua.
FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Timing di conoscenza di presentazione di sogge
Aly Saleh - FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Utile e buono ascoltatore .. Interactive
Ahmed El Kholy - FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Ahmed era molto interattivo e non mi ha pensato di rispondere a qualsiasi tipo di presentazione ben presentazione e flusso liscio del corso
Mohamed Ghowaiba - FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Il corso è molto interessante essere il principale memosa da oggi
mohamed taher - FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Le discussioni per ampliare i nostri orizzonti
FAB banak Egypt
Corso: Introduction to Data Science and AI (using Python)
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Schema generale del corso Reinforcement Learning
Nome del corso
Durata
Overview
Nome del corso
Durata
Overview
21 hours
Overview
This instructor-led, live training in Italia (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 hours
Overview
This instructor-led, live training in Italia (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 hours
Overview
This instructor-led, live training in Italia (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.
Last Updated:
Prossimi corsi Reinforcement Learning
14-12-2021 09:30
21 ore
14-12-2021 09:30
21 ore
23-12-2021 09:30
7 ore
23-12-2021 09:30
7 ore
12-01-2022 09:30
21 ore
Other countries
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