Rome

NobleProg Rome, Via Clelia Bertini Attilj 4, Rome, italy, 34D
CoworkTalenti is located in the north part of Rome, in the discrictTalenti-Montesacro, a few minutes from the highway ring. I...

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Deep Reinforcement Learning with Python

21 Hours
This instructor-led, live training in Rome (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:
  • 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.
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Fundamentals of Reinforcement Learning

21 Hours
This instructor-led, live training in Rome (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.
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Reinforcement Learning with Java

21 Hours
This instructor-led, live training in Rome (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.
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Testimonials (10)

examples and exercises

Kamil
Course - Introduction to Data Science and AI using Python

Machine Translated

The course is very interesting being the main focus nowdays

mohamed taher - 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)

Helpful and good listener .. interactive

Ahmed El Kholy - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

All the information presented

Jose Victor - si
Course - Artificial Intelligence (AI) for Managers

The exercises and examples presented.

Marcos - si
Course - Artificial Intelligence (AI) for Managers

Machine Translated

Machine Learning Themes

Víctor Edgar - si
Course - Artificial Intelligence (AI) for Managers

The teacher's attitude

Ivonne Guadalupe Avendaño Hernandez - si
Course - Artificial Intelligence (AI) for Managers

Machine Translated

The concepts taught were clear, practical and help a lot in getting an idea of how to use this AI & ML topic.

Miguel - si
Course - Artificial Intelligence (AI) for Managers

Machine Translated

The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.

Terry Strachan - Exel Computer Systems plc
Course - OptaPlanner in Practice

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